From 2acfc99a9aa58c67ce31115893b3f2401296d26a Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Sat, 16 Apr 2022 18:20:26 -0500 Subject: [PATCH 01/10] Inicializar archivos --- Taller 2/Scripts/first.ipynb | 191 ++++++++++++++++++++++++++++ Taller 2/data/Datos Insumo (1).xlsx | Bin 919155 -> 919189 bytes 2 files changed, 191 insertions(+) create mode 100644 Taller 2/Scripts/first.ipynb diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb new file mode 100644 index 0000000..a90b707 --- /dev/null +++ b/Taller 2/Scripts/first.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimización del Activo\n", + "Import de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'lubridate' was built under R version 3.6.3\"\n", + "Attaching package: 'lubridate'\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " date, intersect, setdiff, union\n", + "\n", + "Warning message:\n", + "\"package 'tidyr' was built under R version 3.6.3\"" + ] + } + ], + "source": [ + "library(\"readxl\")\n", + "library(\"lubridate\")\n", + "library('tidyr')\n", + "library('ggplot2')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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z_PcF%{@e3(9E#M}ZZpwy*ilE{cK&~3`Yij8fCJ+4yW3^`~dD@u{(iF!BKv0RW&8&#nLf From b61e838eb03e5b681c47fbfc812e453db63ee6f9 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Sat, 16 Apr 2022 18:34:44 -0500 Subject: [PATCH 02/10] Cambio de nombre --- Taller 2/Scripts/first.ipynb | 34 +++++------------- .../data/{Datos Insumo (1).xlsx => data.xlsx} | Bin 2 files changed, 8 insertions(+), 26 deletions(-) rename Taller 2/data/{Datos Insumo (1).xlsx => data.xlsx} (100%) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index a90b707..96adbc7 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -10,30 +10,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"package 'lubridate' was built under R version 3.6.3\"\n", - "Attaching package: 'lubridate'\n", - "\n", - "The following objects are masked from 'package:base':\n", - "\n", - " date, intersect, setdiff, union\n", - "\n", - "Warning message:\n", - "\"package 'tidyr' was built under R version 3.6.3\"" - ] - } - ], + "outputs": [], "source": [ "library(\"readxl\")\n", "library(\"lubridate\")\n", @@ -129,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "metadata": { "vscode": { "languageId": "r" @@ -137,12 +120,12 @@ }, "outputs": [], "source": [ - "path <- \"../Taller 2/data/Datos Insumo (1).xlsx\"" + "path <- '../T1-Riesgo/Taller 2/data/data.xlsx'\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": { "vscode": { "languageId": "r" @@ -151,10 +134,10 @@ "outputs": [ { "ename": "ERROR", - "evalue": "Error: `path` does not exist: '../Taller 2/data/Datos lnsumo (1).xlsx'\n", + "evalue": "Error: `path` does not exist: '../T1-Riesgo/Taller 2/data/data.xlsx'\n", "output_type": "error", "traceback": [ - "Error: `path` does not exist: '../Taller 2/data/Datos lnsumo (1).xlsx'\nTraceback:\n", + "Error: `path` does not exist: '../T1-Riesgo/Taller 2/data/data.xlsx'\nTraceback:\n", "1. excel_sheets(path)", "2. check_file(path)", "3. stop(\"`path` does not exist: \", sQuote(path), call. = FALSE)" @@ -162,8 +145,7 @@ } ], "source": [ - "names <- excel_sheets(path)\n", - "names" + "names <- excel_sheets(path)" ] } ], diff --git a/Taller 2/data/Datos Insumo (1).xlsx b/Taller 2/data/data.xlsx similarity index 100% rename from Taller 2/data/Datos Insumo (1).xlsx rename to Taller 2/data/data.xlsx From 6ebb2f1fffe4a818a4830562f832ab4d30033cf4 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Sat, 16 Apr 2022 19:34:53 -0500 Subject: [PATCH 03/10] Import de bases de datos --- Taller 2/Scripts/first.ipynb | 444 +++++++++++++++++++++++++++++++++-- 1 file changed, 426 insertions(+), 18 deletions(-) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index 96adbc7..78451a5 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -5,18 +5,36 @@ "metadata": {}, "source": [ "# Optimización del Activo\n", - "Import de datos" + "## Import de paquetes y funciones" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'readxl' was built under R version 3.6.3\"Warning message:\n", + "\"package 'lubridate' was built under R version 3.6.3\"\n", + "Attaching package: 'lubridate'\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " date, intersect, setdiff, union\n", + "\n", + "Warning message:\n", + "\"package 'tidyr' was built under R version 3.6.3\"" + ] + } + ], "source": [ "library(\"readxl\")\n", "library(\"lubridate\")\n", @@ -26,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "vscode": { "languageId": "r" @@ -99,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "vscode": { "languageId": "r" @@ -110,9 +128,29 @@ "source(\"helpers.R\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import de bases de datos de los indices" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "path <- 'C:/Users/Diana C Contreras/OneDrive - Universidad de Los Andes/Riesgo Financiero/Talleres/T1-Riesgo/Taller 2/data/data.xlsx'" + ] + }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": { "vscode": { "languageId": "r" @@ -120,12 +158,12 @@ }, "outputs": [], "source": [ - "path <- '../T1-Riesgo/Taller 2/data/data.xlsx'\n" + "tes_cop <- read_excel(path, sheet=1,skip=7, col_names=c('Date','6m','1y','3y','5y','10y','15y'))" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": { "vscode": { "languageId": "r" @@ -133,20 +171,390 @@ }, "outputs": [ { - "ename": "ERROR", - "evalue": "Error: `path` does not exist: '../T1-Riesgo/Taller 2/data/data.xlsx'\n", - "output_type": "error", - "traceback": [ - "Error: `path` does not exist: '../T1-Riesgo/Taller 2/data/data.xlsx'\nTraceback:\n", - "1. excel_sheets(path)", - "2. check_file(path)", - "3. stop(\"`path` does not exist: \", sQuote(path), call. = FALSE)" - ] + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 7
Date6m1y3y5y10y15y
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2012-04-275.40425.53286.00566.41396.92767.3809
2012-04-305.37035.51075.99536.42646.94107.3830
2012-05-015.37495.51766.00446.43126.94307.3839
2012-05-025.41765.55136.06136.42816.93087.3765
2012-05-035.43045.55086.04956.40766.88967.3203
2012-05-045.45705.57366.09116.43636.90427.3267
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A tibble: 6 × 8
Date3m6m1y3y5y10y15y
<dttm><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2009-10-131.14041.21811.50462.56613.48114.70765.3309
2009-10-141.13811.22081.51962.60133.52934.78435.4388
2009-10-151.10551.19321.50412.62953.57334.83085.4545
2009-10-161.21141.28971.56192.61393.54474.78205.4008
2009-10-191.17781.26271.54202.59973.52834.75025.3610
2009-10-201.15931.24211.50962.54543.47394.72145.3203
\n" + ], + "text/latex": [ + "A tibble: 6 × 8\n", + "\\begin{tabular}{llllllll}\n", + " Date & 3m & 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t 2009-10-13 & 1.1404 & 1.2181 & 1.5046 & 2.5661 & 3.4811 & 4.7076 & 5.3309\\\\\n", + "\t 2009-10-14 & 1.1381 & 1.2208 & 1.5196 & 2.6013 & 3.5293 & 4.7843 & 5.4388\\\\\n", + "\t 2009-10-15 & 1.1055 & 1.1932 & 1.5041 & 2.6295 & 3.5733 & 4.8308 & 5.4545\\\\\n", + "\t 2009-10-16 & 1.2114 & 1.2897 & 1.5619 & 2.6139 & 3.5447 & 4.7820 & 5.4008\\\\\n", + "\t 2009-10-19 & 1.1778 & 1.2627 & 1.5420 & 2.5997 & 3.5283 & 4.7502 & 5.3610\\\\\n", + "\t 2009-10-20 & 1.1593 & 1.2421 & 1.5096 & 2.5454 & 3.4739 & 4.7214 & 5.3203\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 8\n", + "\n", + "| Date <dttm> | 3m <dbl> | 6m <dbl> | 1y <dbl> | 3y <dbl> | 5y <dbl> | 10y <dbl> | 15y <dbl> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| 2009-10-13 | 1.1404 | 1.2181 | 1.5046 | 2.5661 | 3.4811 | 4.7076 | 5.3309 |\n", + "| 2009-10-14 | 1.1381 | 1.2208 | 1.5196 | 2.6013 | 3.5293 | 4.7843 | 5.4388 |\n", + "| 2009-10-15 | 1.1055 | 1.1932 | 1.5041 | 2.6295 | 3.5733 | 4.8308 | 5.4545 |\n", + "| 2009-10-16 | 1.2114 | 1.2897 | 1.5619 | 2.6139 | 3.5447 | 4.7820 | 5.4008 |\n", + "| 2009-10-19 | 1.1778 | 1.2627 | 1.5420 | 2.5997 | 3.5283 | 4.7502 | 5.3610 |\n", + "| 2009-10-20 | 1.1593 | 1.2421 | 1.5096 | 2.5454 | 3.4739 | 4.7214 | 5.3203 |\n", + "\n" + ], + "text/plain": [ + " Date 3m 6m 1y 3y 5y 10y 15y \n", + "1 2009-10-13 1.1404 1.2181 1.5046 2.5661 3.4811 4.7076 5.3309\n", + "2 2009-10-14 1.1381 1.2208 1.5196 2.6013 3.5293 4.7843 5.4388\n", + "3 2009-10-15 1.1055 1.1932 1.5041 2.6295 3.5733 4.8308 5.4545\n", + "4 2009-10-16 1.2114 1.2897 1.5619 2.6139 3.5447 4.7820 5.4008\n", + "5 2009-10-19 1.1778 1.2627 1.5420 2.5997 3.5283 4.7502 5.3610\n", + "6 2009-10-20 1.1593 1.2421 1.5096 2.5454 3.4739 4.7214 5.3203" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corp_usa <- read_excel(path, sheet='Corporativo A EEUU',skip=8, col_names=c('Date','3m','6m','1y','3y','5y','10y','15y'))\n", + "head(corp_usa)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 3
Datecolcapspx
<dttm><dbl><dbl>
2009-10-131303.031073.19
2009-10-141294.391092.02
2009-10-151294.111096.56
2009-10-161301.691087.68
2009-10-191330.801097.91
2009-10-201315.971091.06
\n" + ], + "text/latex": [ + "A tibble: 6 × 3\n", + "\\begin{tabular}{lll}\n", + " Date & colcap & spx\\\\\n", + " & & \\\\\n", + "\\hline\n", + "\t 2009-10-13 & 1303.03 & 1073.19\\\\\n", + "\t 2009-10-14 & 1294.39 & 1092.02\\\\\n", + "\t 2009-10-15 & 1294.11 & 1096.56\\\\\n", + "\t 2009-10-16 & 1301.69 & 1087.68\\\\\n", + "\t 2009-10-19 & 1330.80 & 1097.91\\\\\n", + "\t 2009-10-20 & 1315.97 & 1091.06\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 3\n", + "\n", + "| Date <dttm> | colcap <dbl> | spx <dbl> |\n", + "|---|---|---|\n", + "| 2009-10-13 | 1303.03 | 1073.19 |\n", + "| 2009-10-14 | 1294.39 | 1092.02 |\n", + "| 2009-10-15 | 1294.11 | 1096.56 |\n", + "| 2009-10-16 | 1301.69 | 1087.68 |\n", + "| 2009-10-19 | 1330.80 | 1097.91 |\n", + "| 2009-10-20 | 1315.97 | 1091.06 |\n", + "\n" + ], + "text/plain": [ + " Date colcap spx \n", + "1 2009-10-13 1303.03 1073.19\n", + "2 2009-10-14 1294.39 1092.02\n", + "3 2009-10-15 1294.11 1096.56\n", + "4 2009-10-16 1301.69 1087.68\n", + "5 2009-10-19 1330.80 1097.91\n", + "6 2009-10-20 1315.97 1091.06" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index <- read_excel(path, sheet='Indices Accionarios',skip=7, col_names=c('Date','colcap','spx'))\n", + "head(index)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 2
Dateipc
<dttm><dbl>
1954-07-310.02632
1954-08-310.02612
1954-09-300.02576
1954-10-310.02585
1954-11-300.02591
1954-12-310.02605
\n" + ], + "text/latex": [ + "A tibble: 6 × 2\n", + "\\begin{tabular}{ll}\n", + " Date & ipc\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t 1954-07-31 & 0.02632\\\\\n", + "\t 1954-08-31 & 0.02612\\\\\n", + "\t 1954-09-30 & 0.02576\\\\\n", + "\t 1954-10-31 & 0.02585\\\\\n", + "\t 1954-11-30 & 0.02591\\\\\n", + "\t 1954-12-31 & 0.02605\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 2\n", + "\n", + "| Date <dttm> | ipc <dbl> |\n", + "|---|---|\n", + "| 1954-07-31 | 0.02632 |\n", + "| 1954-08-31 | 0.02612 |\n", + "| 1954-09-30 | 0.02576 |\n", + "| 1954-10-31 | 0.02585 |\n", + "| 1954-11-30 | 0.02591 |\n", + "| 1954-12-31 | 0.02605 |\n", + "\n" + ], + "text/plain": [ + " Date ipc \n", + "1 1954-07-31 0.02632\n", + "2 1954-08-31 0.02612\n", + "3 1954-09-30 0.02576\n", + "4 1954-10-31 0.02585\n", + "5 1954-11-30 0.02591\n", + "6 1954-12-31 0.02605" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "names <- excel_sheets(path)" + "ipc <- read_excel(path, sheet='IPC',skip=1, col_names=c('Date','ipc'))\n", + "head(ipc)" ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A tibble: 6 × 2
Datetrm
<dttm><dbl>
1991-11-27693.32
1991-11-28693.99
1991-11-29694.70
1991-11-30694.70
1991-12-01643.42
1991-12-02643.42
\n" + ], + "text/latex": [ + "A tibble: 6 × 2\n", + "\\begin{tabular}{ll}\n", + " Date & trm\\\\\n", + " & \\\\\n", + "\\hline\n", + "\t 1991-11-27 & 693.32\\\\\n", + "\t 1991-11-28 & 693.99\\\\\n", + "\t 1991-11-29 & 694.70\\\\\n", + "\t 1991-11-30 & 694.70\\\\\n", + "\t 1991-12-01 & 643.42\\\\\n", + "\t 1991-12-02 & 643.42\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A tibble: 6 × 2\n", + "\n", + "| Date <dttm> | trm <dbl> |\n", + "|---|---|\n", + "| 1991-11-27 | 693.32 |\n", + "| 1991-11-28 | 693.99 |\n", + "| 1991-11-29 | 694.70 |\n", + "| 1991-11-30 | 694.70 |\n", + "| 1991-12-01 | 643.42 |\n", + "| 1991-12-02 | 643.42 |\n", + "\n" + ], + "text/plain": [ + " Date trm \n", + "1 1991-11-27 693.32\n", + "2 1991-11-28 693.99\n", + "3 1991-11-29 694.70\n", + "4 1991-11-30 694.70\n", + "5 1991-12-01 643.42\n", + "6 1991-12-02 643.42" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trm <- read_excel(path, sheet='TRM',skip=1, col_names=c('Date','trm'))\n", + "head(trm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulaciones a futuro" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [] } ], "metadata": { From 01fc0f132c806af465c08f95e35d254cee925589 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Sun, 24 Apr 2022 20:15:59 -0500 Subject: [PATCH 04/10] =?UTF-8?q?Punto=20A=20mortalidad.=20Proyecci=C3=B3n?= =?UTF-8?q?=20a=2020=20a=C3=B1os?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Taller 2/data/Mortality.xlsx | Bin 0 -> 69565 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Taller 2/data/Mortality.xlsx diff --git a/Taller 2/data/Mortality.xlsx b/Taller 2/data/Mortality.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..a4ebe9f77d3706e03ba61fe326b4bc8635dfc321 GIT 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2022 20:21:53 -0500 Subject: [PATCH 05/10] Se organiza y calcula Nelson Sieguel --- Taller 2/Scripts/first.ipynb | 670 +++++++++++++++++++++++++---------- 1 file changed, 482 insertions(+), 188 deletions(-) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index 78451a5..0e1ff6a 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -31,7 +31,20 @@ " date, intersect, setdiff, union\n", "\n", "Warning message:\n", - "\"package 'tidyr' was built under R version 3.6.3\"" + "\"package 'tidyr' was built under R version 3.6.3\"Warning message:\n", + "\"package 'YieldCurve' was built under R version 3.6.3\"Loading required package: xts\n", + "Loading required package: zoo\n", + "Warning message:\n", + "\"package 'zoo' was built under R version 3.6.3\"\n", + "Attaching package: 'zoo'\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " as.Date, as.Date.numeric\n", + "\n", + "Registered S3 method overwritten by 'xts':\n", + " method from\n", + " as.zoo.xts zoo \n" ] } ], @@ -39,7 +52,8 @@ "library(\"readxl\")\n", "library(\"lubridate\")\n", "library('tidyr')\n", - "library('ggplot2')" + "library('ggplot2')\n", + "library('YieldCurve')" ] }, { @@ -59,11 +73,14 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "

  1. '.GlobalEnv'
  2. 'package:ggplot2'
  3. 'package:tidyr'
  4. 'package:lubridate'
  5. 'package:readxl'
  6. 'jupyter:irkernel'
  7. 'jupyter:irkernel'
  8. 'package:stats'
  9. 'package:graphics'
  10. 'package:grDevices'
  11. 'package:utils'
  12. 'package:datasets'
  13. 'package:methods'
  14. 'Autoloads'
  15. 'package:base'
\n" + "
  1. '.GlobalEnv'
  2. 'package:YieldCurve'
  3. 'package:xts'
  4. 'package:zoo'
  5. 'package:ggplot2'
  6. 'package:tidyr'
  7. 'package:lubridate'
  8. 'package:readxl'
  9. 'jupyter:irkernel'
  10. 'jupyter:irkernel'
  11. 'package:stats'
  12. 'package:graphics'
  13. 'package:grDevices'
  14. 'package:utils'
  15. 'package:datasets'
  16. 'package:methods'
  17. 'Autoloads'
  18. 'package:base'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item '.GlobalEnv'\n", + "\\item 'package:YieldCurve'\n", + "\\item 'package:xts'\n", + "\\item 'package:zoo'\n", "\\item 'package:ggplot2'\n", "\\item 'package:tidyr'\n", "\\item 'package:lubridate'\n", @@ -82,29 +99,33 @@ ], "text/markdown": [ "1. '.GlobalEnv'\n", - "2. 'package:ggplot2'\n", - "3. 'package:tidyr'\n", - "4. 'package:lubridate'\n", - "5. 'package:readxl'\n", - "6. 'jupyter:irkernel'\n", - "7. 'jupyter:irkernel'\n", - "8. 'package:stats'\n", - "9. 'package:graphics'\n", - "10. 'package:grDevices'\n", - "11. 'package:utils'\n", - "12. 'package:datasets'\n", - "13. 'package:methods'\n", - "14. 'Autoloads'\n", - "15. 'package:base'\n", + "2. 'package:YieldCurve'\n", + "3. 'package:xts'\n", + "4. 'package:zoo'\n", + "5. 'package:ggplot2'\n", + "6. 'package:tidyr'\n", + "7. 'package:lubridate'\n", + "8. 'package:readxl'\n", + "9. 'jupyter:irkernel'\n", + "10. 'jupyter:irkernel'\n", + "11. 'package:stats'\n", + "12. 'package:graphics'\n", + "13. 'package:grDevices'\n", + "14. 'package:utils'\n", + "15. 'package:datasets'\n", + "16. 'package:methods'\n", + "17. 'Autoloads'\n", + "18. 'package:base'\n", "\n", "\n" ], "text/plain": [ - " [1] \".GlobalEnv\" \"package:ggplot2\" \"package:tidyr\" \n", - " [4] \"package:lubridate\" \"package:readxl\" \"jupyter:irkernel\" \n", - " [7] \"jupyter:irkernel\" \"package:stats\" \"package:graphics\" \n", - "[10] \"package:grDevices\" \"package:utils\" \"package:datasets\" \n", - "[13] \"package:methods\" \"Autoloads\" \"package:base\" " + " [1] \".GlobalEnv\" \"package:YieldCurve\" \"package:xts\" \n", + " [4] \"package:zoo\" \"package:ggplot2\" \"package:tidyr\" \n", + " [7] \"package:lubridate\" \"package:readxl\" \"jupyter:irkernel\" \n", + "[10] \"jupyter:irkernel\" \"package:stats\" \"package:graphics\" \n", + "[13] \"package:grDevices\" \"package:utils\" \"package:datasets\" \n", + "[16] \"package:methods\" \"Autoloads\" \"package:base\" " ] }, "metadata": {}, @@ -132,7 +153,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import de bases de datos de los indices" + "## Import de bases de datos de los indices\n", + "En esta sección se importan todos los datos de las tasas de interés de los bonos y activos requeridos. \n", + "Adicionalmente, se encuentra la estimación de Nelson Sieguel para descomponer el comportamiento de las tasas." ] }, { @@ -148,6 +171,13 @@ "path <- 'C:/Users/Diana C Contreras/OneDrive - Universidad de Los Andes/Riesgo Financiero/Talleres/T1-Riesgo/Taller 2/data/data.xlsx'" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Empezamos con los bonos TES en pesos." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -174,57 +204,55 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A tibble: 6 × 7A matrix: 6 × 6 of type dbl
Date6m1y3y5y10y15y
<dttm><dbl><dbl><dbl><dbl><dbl><dbl>
6m1y3y5y10y15y
2012-04-275.40425.53286.00566.41396.92767.3809
2012-04-305.37035.51075.99536.42646.94107.3830
2012-05-015.37495.51766.00446.43126.94307.3839
2012-05-025.41765.55136.06136.42816.93087.3765
2012-05-035.43045.55086.04956.40766.88967.3203
2012-05-045.45705.57366.09116.43636.90427.3267
5.40425.53286.00566.41396.92767.3809
5.37035.51075.99536.42646.94107.3830
5.37495.51766.00446.43126.94307.3839
5.41765.55136.06136.42816.93087.3765
5.43045.55086.04956.40766.88967.3203
5.45705.57366.09116.43636.90427.3267
\n" ], "text/latex": [ - "A tibble: 6 × 7\n", - "\\begin{tabular}{lllllll}\n", - " Date & 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", - " & & & & & & \\\\\n", + "A matrix: 6 × 6 of type dbl\n", + "\\begin{tabular}{llllll}\n", + " 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", "\\hline\n", - "\t 2012-04-27 & 5.4042 & 5.5328 & 6.0056 & 6.4139 & 6.9276 & 7.3809\\\\\n", - "\t 2012-04-30 & 5.3703 & 5.5107 & 5.9953 & 6.4264 & 6.9410 & 7.3830\\\\\n", - "\t 2012-05-01 & 5.3749 & 5.5176 & 6.0044 & 6.4312 & 6.9430 & 7.3839\\\\\n", - "\t 2012-05-02 & 5.4176 & 5.5513 & 6.0613 & 6.4281 & 6.9308 & 7.3765\\\\\n", - "\t 2012-05-03 & 5.4304 & 5.5508 & 6.0495 & 6.4076 & 6.8896 & 7.3203\\\\\n", - "\t 2012-05-04 & 5.4570 & 5.5736 & 6.0911 & 6.4363 & 6.9042 & 7.3267\\\\\n", + "\t 5.4042 & 5.5328 & 6.0056 & 6.4139 & 6.9276 & 7.3809\\\\\n", + "\t 5.3703 & 5.5107 & 5.9953 & 6.4264 & 6.9410 & 7.3830\\\\\n", + "\t 5.3749 & 5.5176 & 6.0044 & 6.4312 & 6.9430 & 7.3839\\\\\n", + "\t 5.4176 & 5.5513 & 6.0613 & 6.4281 & 6.9308 & 7.3765\\\\\n", + "\t 5.4304 & 5.5508 & 6.0495 & 6.4076 & 6.8896 & 7.3203\\\\\n", + "\t 5.4570 & 5.5736 & 6.0911 & 6.4363 & 6.9042 & 7.3267\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A tibble: 6 × 7\n", + "A matrix: 6 × 6 of type dbl\n", "\n", - "| Date <dttm> | 6m <dbl> | 1y <dbl> | 3y <dbl> | 5y <dbl> | 10y <dbl> | 15y <dbl> |\n", - "|---|---|---|---|---|---|---|\n", - "| 2012-04-27 | 5.4042 | 5.5328 | 6.0056 | 6.4139 | 6.9276 | 7.3809 |\n", - "| 2012-04-30 | 5.3703 | 5.5107 | 5.9953 | 6.4264 | 6.9410 | 7.3830 |\n", - "| 2012-05-01 | 5.3749 | 5.5176 | 6.0044 | 6.4312 | 6.9430 | 7.3839 |\n", - "| 2012-05-02 | 5.4176 | 5.5513 | 6.0613 | 6.4281 | 6.9308 | 7.3765 |\n", - "| 2012-05-03 | 5.4304 | 5.5508 | 6.0495 | 6.4076 | 6.8896 | 7.3203 |\n", - "| 2012-05-04 | 5.4570 | 5.5736 | 6.0911 | 6.4363 | 6.9042 | 7.3267 |\n", + "| 6m | 1y | 3y | 5y | 10y | 15y |\n", + "|---|---|---|---|---|---|\n", + "| 5.4042 | 5.5328 | 6.0056 | 6.4139 | 6.9276 | 7.3809 |\n", + "| 5.3703 | 5.5107 | 5.9953 | 6.4264 | 6.9410 | 7.3830 |\n", + "| 5.3749 | 5.5176 | 6.0044 | 6.4312 | 6.9430 | 7.3839 |\n", + "| 5.4176 | 5.5513 | 6.0613 | 6.4281 | 6.9308 | 7.3765 |\n", + "| 5.4304 | 5.5508 | 6.0495 | 6.4076 | 6.8896 | 7.3203 |\n", + "| 5.4570 | 5.5736 | 6.0911 | 6.4363 | 6.9042 | 7.3267 |\n", "\n" ], "text/plain": [ - " Date 6m 1y 3y 5y 10y 15y \n", - "1 2012-04-27 5.4042 5.5328 6.0056 6.4139 6.9276 7.3809\n", - "2 2012-04-30 5.3703 5.5107 5.9953 6.4264 6.9410 7.3830\n", - "3 2012-05-01 5.3749 5.5176 6.0044 6.4312 6.9430 7.3839\n", - "4 2012-05-02 5.4176 5.5513 6.0613 6.4281 6.9308 7.3765\n", - "5 2012-05-03 5.4304 5.5508 6.0495 6.4076 6.8896 7.3203\n", - "6 2012-05-04 5.4570 5.5736 6.0911 6.4363 6.9042 7.3267" + " 6m 1y 3y 5y 10y 15y \n", + "[1,] 5.4042 5.5328 6.0056 6.4139 6.9276 7.3809\n", + "[2,] 5.3703 5.5107 5.9953 6.4264 6.9410 7.3830\n", + "[3,] 5.3749 5.5176 6.0044 6.4312 6.9430 7.3839\n", + "[4,] 5.4176 5.5513 6.0613 6.4281 6.9308 7.3765\n", + "[5,] 5.4304 5.5508 6.0495 6.4076 6.8896 7.3203\n", + "[6,] 5.4570 5.5736 6.0911 6.4363 6.9042 7.3267" ] }, "metadata": {}, @@ -233,9 +261,12 @@ ], "source": [ "tes_cop <- tes_cop[tes_cop$Date >= \"2012-04-26\",]\n", - "yields <- c('6m','1y','3y','5y','10y','15y')\n", - "tes_cop[, yields] <- sapply(tes_cop[, yields], as.numeric)\n", - "head(tes_cop)" + "tes_cop <- as.data.frame(tes_cop)\n", + "rownames(tes_cop) <- tes_cop$Date\n", + "tes_cop <- tes_cop[,-c(1)]\n", + "tes_cop <- sapply(tes_cop, as.numeric)\n", + "\n", + "head(tes_cop)\n" ] }, { @@ -251,40 +282,207 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A tibble: 6 × 8A matrix: 6 × 4 of type dbl
Date3m6m1y3y5y10y15y
<dttm><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
beta_0beta_1beta_2lambda
2009-10-131.14041.21811.50462.56613.48114.70765.3309
2009-10-141.13811.22081.51962.60133.52934.78435.4388
2009-10-151.10551.19321.50412.62953.57334.83085.4545
2009-10-161.21141.28971.56192.61393.54474.78205.4008
2009-10-191.17781.26271.54202.59973.52834.75025.3610
2009-10-201.15931.24211.50962.54543.47394.72145.3203
8.378015-3.115947 0.036157500.1887807
8.308243-3.090228-0.070084140.2109684
8.295119-3.071663-0.041081350.2109684
8.253432-2.979203-0.043223110.2109684
8.161660-2.874677-0.024016360.2109684
8.095042-2.787337-0.025973860.2241687
\n" ], "text/latex": [ - "A tibble: 6 × 8\n", - "\\begin{tabular}{llllllll}\n", - " Date & 3m & 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", - " & & & & & & & \\\\\n", + "A matrix: 6 × 4 of type dbl\n", + "\\begin{tabular}{llll}\n", + " beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", "\\hline\n", - "\t 2009-10-13 & 1.1404 & 1.2181 & 1.5046 & 2.5661 & 3.4811 & 4.7076 & 5.3309\\\\\n", - "\t 2009-10-14 & 1.1381 & 1.2208 & 1.5196 & 2.6013 & 3.5293 & 4.7843 & 5.4388\\\\\n", - "\t 2009-10-15 & 1.1055 & 1.1932 & 1.5041 & 2.6295 & 3.5733 & 4.8308 & 5.4545\\\\\n", - "\t 2009-10-16 & 1.2114 & 1.2897 & 1.5619 & 2.6139 & 3.5447 & 4.7820 & 5.4008\\\\\n", - "\t 2009-10-19 & 1.1778 & 1.2627 & 1.5420 & 2.5997 & 3.5283 & 4.7502 & 5.3610\\\\\n", - "\t 2009-10-20 & 1.1593 & 1.2421 & 1.5096 & 2.5454 & 3.4739 & 4.7214 & 5.3203\\\\\n", + "\t 8.378015 & -3.115947 & 0.03615750 & 0.1887807\\\\\n", + "\t 8.308243 & -3.090228 & -0.07008414 & 0.2109684\\\\\n", + "\t 8.295119 & -3.071663 & -0.04108135 & 0.2109684\\\\\n", + "\t 8.253432 & -2.979203 & -0.04322311 & 0.2109684\\\\\n", + "\t 8.161660 & -2.874677 & -0.02401636 & 0.2109684\\\\\n", + "\t 8.095042 & -2.787337 & -0.02597386 & 0.2241687\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A tibble: 6 × 8\n", + "A matrix: 6 × 4 of type dbl\n", "\n", - "| Date <dttm> | 3m <dbl> | 6m <dbl> | 1y <dbl> | 3y <dbl> | 5y <dbl> | 10y <dbl> | 15y <dbl> |\n", + "| beta_0 | beta_1 | beta_2 | lambda |\n", + "|---|---|---|---|\n", + "| 8.378015 | -3.115947 | 0.03615750 | 0.1887807 |\n", + "| 8.308243 | -3.090228 | -0.07008414 | 0.2109684 |\n", + "| 8.295119 | -3.071663 | -0.04108135 | 0.2109684 |\n", + "| 8.253432 | -2.979203 | -0.04322311 | 0.2109684 |\n", + "| 8.161660 | -2.874677 | -0.02401636 | 0.2109684 |\n", + "| 8.095042 | -2.787337 | -0.02597386 | 0.2241687 |\n", + "\n" + ], + "text/plain": [ + " beta_0 beta_1 beta_2 lambda \n", + "[1,] 8.378015 -3.115947 0.03615750 0.1887807\n", + "[2,] 8.308243 -3.090228 -0.07008414 0.2109684\n", + "[3,] 8.295119 -3.071663 -0.04108135 0.2109684\n", + "[4,] 8.253432 -2.979203 -0.04322311 0.2109684\n", + "[5,] 8.161660 -2.874677 -0.02401636 0.2109684\n", + "[6,] 8.095042 -2.787337 -0.02597386 0.2241687" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tes_maturity <- c(0.5,1,3,5,10,15)\n", + "tes_cop_ns <- Nelson.Siegel( rate=tes_cop,\tmaturity=tes_maturity)\n", + "head(tes_cop_ns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TES en UVR \n", + "Notese que en este caso se importan directamente los resultados de Nelson Sieguel" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
beta_0beta_1beta_2lambda
<dbl><dbl><dbl><dbl>
2022-04-080.09647801-0.031194060.045114673.7
2022-04-070.09405698-0.029692440.042260963.7
2022-04-060.09469592-0.029902990.042488663.7
2022-04-050.09408631-0.028839650.041866473.7
2022-04-040.09664698-0.033902160.043123843.7
2022-04-010.09685086-0.032173700.044684123.7
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t2022-04-08 & 0.09647801 & -0.03119406 & 0.04511467 & 3.7\\\\\n", + "\t2022-04-07 & 0.09405698 & -0.02969244 & 0.04226096 & 3.7\\\\\n", + "\t2022-04-06 & 0.09469592 & -0.02990299 & 0.04248866 & 3.7\\\\\n", + "\t2022-04-05 & 0.09408631 & -0.02883965 & 0.04186647 & 3.7\\\\\n", + "\t2022-04-04 & 0.09664698 & -0.03390216 & 0.04312384 & 3.7\\\\\n", + "\t2022-04-01 & 0.09685086 & -0.03217370 & 0.04468412 & 3.7\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | beta_0 <dbl> | beta_1 <dbl> | beta_2 <dbl> | lambda <dbl> |\n", + "|---|---|---|---|---|\n", + "| 2022-04-08 | 0.09647801 | -0.03119406 | 0.04511467 | 3.7 |\n", + "| 2022-04-07 | 0.09405698 | -0.02969244 | 0.04226096 | 3.7 |\n", + "| 2022-04-06 | 0.09469592 | -0.02990299 | 0.04248866 | 3.7 |\n", + "| 2022-04-05 | 0.09408631 | -0.02883965 | 0.04186647 | 3.7 |\n", + "| 2022-04-04 | 0.09664698 | -0.03390216 | 0.04312384 | 3.7 |\n", + "| 2022-04-01 | 0.09685086 | -0.03217370 | 0.04468412 | 3.7 |\n", + "\n" + ], + "text/plain": [ + " beta_0 beta_1 beta_2 lambda\n", + "2022-04-08 0.09647801 -0.03119406 0.04511467 3.7 \n", + "2022-04-07 0.09405698 -0.02969244 0.04226096 3.7 \n", + "2022-04-06 0.09469592 -0.02990299 0.04248866 3.7 \n", + "2022-04-05 0.09408631 -0.02883965 0.04186647 3.7 \n", + "2022-04-04 0.09664698 -0.03390216 0.04312384 3.7 \n", + "2022-04-01 0.09685086 -0.03217370 0.04468412 3.7 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tes_uvr_ns <- read_excel(path, sheet='TES UVR Betas',skip=1, col_names=c('Date','beta_0','beta_1','beta_2','lambda'))\n", + "tes_uvr_ns <- as.data.frame(tes_uvr_ns)\n", + "rownames(tes_uvr_ns) <- tes_uvr_ns$Date\n", + "tes_uvr_ns <- tes_uvr_ns[,-c(1)]\n", + "head(tes_uvr_ns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Corporativo a EEUU" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 7
3m6m1y3y5y10y15y
<dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2009-10-131.14041.21811.50462.56613.48114.70765.3309
2009-10-141.13811.22081.51962.60133.52934.78435.4388
2009-10-151.10551.19321.50412.62953.57334.83085.4545
2009-10-161.21141.28971.56192.61393.54474.78205.4008
2009-10-191.17781.26271.54202.59973.52834.75025.3610
2009-10-201.15931.24211.50962.54543.47394.72145.3203
\n" + ], + "text/latex": [ + "A data.frame: 6 × 7\n", + "\\begin{tabular}{r|lllllll}\n", + " & 3m & 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t2009-10-13 & 1.1404 & 1.2181 & 1.5046 & 2.5661 & 3.4811 & 4.7076 & 5.3309\\\\\n", + "\t2009-10-14 & 1.1381 & 1.2208 & 1.5196 & 2.6013 & 3.5293 & 4.7843 & 5.4388\\\\\n", + "\t2009-10-15 & 1.1055 & 1.1932 & 1.5041 & 2.6295 & 3.5733 & 4.8308 & 5.4545\\\\\n", + "\t2009-10-16 & 1.2114 & 1.2897 & 1.5619 & 2.6139 & 3.5447 & 4.7820 & 5.4008\\\\\n", + "\t2009-10-19 & 1.1778 & 1.2627 & 1.5420 & 2.5997 & 3.5283 & 4.7502 & 5.3610\\\\\n", + "\t2009-10-20 & 1.1593 & 1.2421 & 1.5096 & 2.5454 & 3.4739 & 4.7214 & 5.3203\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 7\n", + "\n", + "| | 3m <dbl> | 6m <dbl> | 1y <dbl> | 3y <dbl> | 5y <dbl> | 10y <dbl> | 15y <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| 2009-10-13 | 1.1404 | 1.2181 | 1.5046 | 2.5661 | 3.4811 | 4.7076 | 5.3309 |\n", "| 2009-10-14 | 1.1381 | 1.2208 | 1.5196 | 2.6013 | 3.5293 | 4.7843 | 5.4388 |\n", @@ -295,13 +493,13 @@ "\n" ], "text/plain": [ - " Date 3m 6m 1y 3y 5y 10y 15y \n", - "1 2009-10-13 1.1404 1.2181 1.5046 2.5661 3.4811 4.7076 5.3309\n", - "2 2009-10-14 1.1381 1.2208 1.5196 2.6013 3.5293 4.7843 5.4388\n", - "3 2009-10-15 1.1055 1.1932 1.5041 2.6295 3.5733 4.8308 5.4545\n", - "4 2009-10-16 1.2114 1.2897 1.5619 2.6139 3.5447 4.7820 5.4008\n", - "5 2009-10-19 1.1778 1.2627 1.5420 2.5997 3.5283 4.7502 5.3610\n", - "6 2009-10-20 1.1593 1.2421 1.5096 2.5454 3.4739 4.7214 5.3203" + " 3m 6m 1y 3y 5y 10y 15y \n", + "2009-10-13 1.1404 1.2181 1.5046 2.5661 3.4811 4.7076 5.3309\n", + "2009-10-14 1.1381 1.2208 1.5196 2.6013 3.5293 4.7843 5.4388\n", + "2009-10-15 1.1055 1.1932 1.5041 2.6295 3.5733 4.8308 5.4545\n", + "2009-10-16 1.2114 1.2897 1.5619 2.6139 3.5447 4.7820 5.4008\n", + "2009-10-19 1.1778 1.2627 1.5420 2.5997 3.5283 4.7502 5.3610\n", + "2009-10-20 1.1593 1.2421 1.5096 2.5454 3.4739 4.7214 5.3203" ] }, "metadata": {}, @@ -310,12 +508,99 @@ ], "source": [ "corp_usa <- read_excel(path, sheet='Corporativo A EEUU',skip=8, col_names=c('Date','3m','6m','1y','3y','5y','10y','15y'))\n", + "\n", + "corp_usa <- as.data.frame(corp_usa)\n", + "rownames(corp_usa) <- corp_usa$Date\n", + "corp_usa <- corp_usa[,-c(1)]\n", "head(corp_usa)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
beta_0beta_1beta_2lambda
<dbl><dbl><dbl><dbl>
2009-10-136.461855-5.425669-4.1697060.5517879
2009-10-146.731600-5.748756-3.6434430.4781896
2009-10-156.607364-5.625489-4.1180860.5517879
2009-10-166.550958-5.437508-4.2697830.5517879
2009-10-196.495381-5.421099-4.1525600.5517879
2009-10-206.476177-5.408359-4.2834470.5517879
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t2009-10-13 & 6.461855 & -5.425669 & -4.169706 & 0.5517879\\\\\n", + "\t2009-10-14 & 6.731600 & -5.748756 & -3.643443 & 0.4781896\\\\\n", + "\t2009-10-15 & 6.607364 & -5.625489 & -4.118086 & 0.5517879\\\\\n", + "\t2009-10-16 & 6.550958 & -5.437508 & -4.269783 & 0.5517879\\\\\n", + "\t2009-10-19 & 6.495381 & -5.421099 & -4.152560 & 0.5517879\\\\\n", + "\t2009-10-20 & 6.476177 & -5.408359 & -4.283447 & 0.5517879\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | beta_0 <dbl> | beta_1 <dbl> | beta_2 <dbl> | lambda <dbl> |\n", + "|---|---|---|---|---|\n", + "| 2009-10-13 | 6.461855 | -5.425669 | -4.169706 | 0.5517879 |\n", + "| 2009-10-14 | 6.731600 | -5.748756 | -3.643443 | 0.4781896 |\n", + "| 2009-10-15 | 6.607364 | -5.625489 | -4.118086 | 0.5517879 |\n", + "| 2009-10-16 | 6.550958 | -5.437508 | -4.269783 | 0.5517879 |\n", + "| 2009-10-19 | 6.495381 | -5.421099 | -4.152560 | 0.5517879 |\n", + "| 2009-10-20 | 6.476177 | -5.408359 | -4.283447 | 0.5517879 |\n", + "\n" + ], + "text/plain": [ + " beta_0 beta_1 beta_2 lambda \n", + "2009-10-13 6.461855 -5.425669 -4.169706 0.5517879\n", + "2009-10-14 6.731600 -5.748756 -3.643443 0.4781896\n", + "2009-10-15 6.607364 -5.625489 -4.118086 0.5517879\n", + "2009-10-16 6.550958 -5.437508 -4.269783 0.5517879\n", + "2009-10-19 6.495381 -5.421099 -4.152560 0.5517879\n", + "2009-10-20 6.476177 -5.408359 -4.283447 0.5517879" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corp_usa_maturity <- c(3/12,0.5,1,3,5,10,15)\n", + "corp_usa_ns <- Nelson.Siegel( rate=corp_usa,\tmaturity=corp_usa_maturity)\n", + "head(corp_usa_ns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ETF's de seguimiento de la economía colombiana (COLCAP) y estadounidense (SPX)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": { "vscode": { "languageId": "r" @@ -326,40 +611,40 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A tibble: 6 × 3A data.frame: 6 × 2
Datecolcapspx
<dttm><dbl><dbl>
colcapspx
<dbl><dbl>
2009-10-131303.031073.19
2009-10-141294.391092.02
2009-10-151294.111096.56
2009-10-161301.691087.68
2009-10-191330.801097.91
2009-10-201315.971091.06
2009-10-131303.031073.19
2009-10-141294.391092.02
2009-10-151294.111096.56
2009-10-161301.691087.68
2009-10-191330.801097.91
2009-10-201315.971091.06
\n" ], "text/latex": [ - "A tibble: 6 × 3\n", - "\\begin{tabular}{lll}\n", - " Date & colcap & spx\\\\\n", - " & & \\\\\n", + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & colcap & spx\\\\\n", + " & & \\\\\n", "\\hline\n", - "\t 2009-10-13 & 1303.03 & 1073.19\\\\\n", - "\t 2009-10-14 & 1294.39 & 1092.02\\\\\n", - "\t 2009-10-15 & 1294.11 & 1096.56\\\\\n", - "\t 2009-10-16 & 1301.69 & 1087.68\\\\\n", - "\t 2009-10-19 & 1330.80 & 1097.91\\\\\n", - "\t 2009-10-20 & 1315.97 & 1091.06\\\\\n", + "\t2009-10-13 & 1303.03 & 1073.19\\\\\n", + "\t2009-10-14 & 1294.39 & 1092.02\\\\\n", + "\t2009-10-15 & 1294.11 & 1096.56\\\\\n", + "\t2009-10-16 & 1301.69 & 1087.68\\\\\n", + "\t2009-10-19 & 1330.80 & 1097.91\\\\\n", + "\t2009-10-20 & 1315.97 & 1091.06\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A tibble: 6 × 3\n", + "A data.frame: 6 × 2\n", "\n", - "| Date <dttm> | colcap <dbl> | spx <dbl> |\n", + "| | colcap <dbl> | spx <dbl> |\n", "|---|---|---|\n", "| 2009-10-13 | 1303.03 | 1073.19 |\n", "| 2009-10-14 | 1294.39 | 1092.02 |\n", @@ -370,13 +655,13 @@ "\n" ], "text/plain": [ - " Date colcap spx \n", - "1 2009-10-13 1303.03 1073.19\n", - "2 2009-10-14 1294.39 1092.02\n", - "3 2009-10-15 1294.11 1096.56\n", - "4 2009-10-16 1301.69 1087.68\n", - "5 2009-10-19 1330.80 1097.91\n", - "6 2009-10-20 1315.97 1091.06" + " colcap spx \n", + "2009-10-13 1303.03 1073.19\n", + "2009-10-14 1294.39 1092.02\n", + "2009-10-15 1294.11 1096.56\n", + "2009-10-16 1301.69 1087.68\n", + "2009-10-19 1330.80 1097.91\n", + "2009-10-20 1315.97 1091.06" ] }, "metadata": {}, @@ -385,12 +670,22 @@ ], "source": [ "index <- read_excel(path, sheet='Indices Accionarios',skip=7, col_names=c('Date','colcap','spx'))\n", + "index <- as.data.frame(index)\n", + "rownames(index) <- index$Date\n", + "index <- index[,-c(1)]\n", "head(index)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indice de Precios al consumidor" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": { "vscode": { "languageId": "r" @@ -401,47 +696,47 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A tibble: 6 × 2A data.frame: 6 × 2
Dateipc
<dttm><dbl>
Dateipc
<dttm><dbl>
1954-07-310.02632
1954-08-310.02612
1954-09-300.02576
1954-10-310.02585
1954-11-300.02591
1954-12-310.02605
11954-07-310.02632
21954-08-310.02612
31954-09-300.02576
41954-10-310.02585
51954-11-300.02591
61954-12-310.02605
\n" ], "text/latex": [ - "A tibble: 6 × 2\n", - "\\begin{tabular}{ll}\n", - " Date & ipc\\\\\n", - " & \\\\\n", + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & Date & ipc\\\\\n", + " & & \\\\\n", "\\hline\n", - "\t 1954-07-31 & 0.02632\\\\\n", - "\t 1954-08-31 & 0.02612\\\\\n", - "\t 1954-09-30 & 0.02576\\\\\n", - "\t 1954-10-31 & 0.02585\\\\\n", - "\t 1954-11-30 & 0.02591\\\\\n", - "\t 1954-12-31 & 0.02605\\\\\n", + "\t1 & 1954-07-31 & 0.02632\\\\\n", + "\t2 & 1954-08-31 & 0.02612\\\\\n", + "\t3 & 1954-09-30 & 0.02576\\\\\n", + "\t4 & 1954-10-31 & 0.02585\\\\\n", + "\t5 & 1954-11-30 & 0.02591\\\\\n", + "\t6 & 1954-12-31 & 0.02605\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A tibble: 6 × 2\n", + "A data.frame: 6 × 2\n", "\n", - "| Date <dttm> | ipc <dbl> |\n", - "|---|---|\n", - "| 1954-07-31 | 0.02632 |\n", - "| 1954-08-31 | 0.02612 |\n", - "| 1954-09-30 | 0.02576 |\n", - "| 1954-10-31 | 0.02585 |\n", - "| 1954-11-30 | 0.02591 |\n", - "| 1954-12-31 | 0.02605 |\n", + "| | Date <dttm> | ipc <dbl> |\n", + "|---|---|---|\n", + "| 1 | 1954-07-31 | 0.02632 |\n", + "| 2 | 1954-08-31 | 0.02612 |\n", + "| 3 | 1954-09-30 | 0.02576 |\n", + "| 4 | 1954-10-31 | 0.02585 |\n", + "| 5 | 1954-11-30 | 0.02591 |\n", + "| 6 | 1954-12-31 | 0.02605 |\n", "\n" ], "text/plain": [ @@ -460,12 +755,21 @@ ], "source": [ "ipc <- read_excel(path, sheet='IPC',skip=1, col_names=c('Date','ipc'))\n", + "ipc <- as.data.frame(ipc)\n", + "\n", "head(ipc)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tasa de cambio" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": { "vscode": { "languageId": "r" @@ -476,47 +780,47 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A tibble: 6 × 2A data.frame: 6 × 2
Datetrm
<dttm><dbl>
Datetrm
<dttm><dbl>
1991-11-27693.32
1991-11-28693.99
1991-11-29694.70
1991-11-30694.70
1991-12-01643.42
1991-12-02643.42
11991-11-27693.32
21991-11-28693.99
31991-11-29694.70
41991-11-30694.70
51991-12-01643.42
61991-12-02643.42
\n" ], "text/latex": [ - "A tibble: 6 × 2\n", - "\\begin{tabular}{ll}\n", - " Date & trm\\\\\n", - " & \\\\\n", + "A data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & Date & trm\\\\\n", + " & & \\\\\n", "\\hline\n", - "\t 1991-11-27 & 693.32\\\\\n", - "\t 1991-11-28 & 693.99\\\\\n", - "\t 1991-11-29 & 694.70\\\\\n", - "\t 1991-11-30 & 694.70\\\\\n", - "\t 1991-12-01 & 643.42\\\\\n", - "\t 1991-12-02 & 643.42\\\\\n", + "\t1 & 1991-11-27 & 693.32\\\\\n", + "\t2 & 1991-11-28 & 693.99\\\\\n", + "\t3 & 1991-11-29 & 694.70\\\\\n", + "\t4 & 1991-11-30 & 694.70\\\\\n", + "\t5 & 1991-12-01 & 643.42\\\\\n", + "\t6 & 1991-12-02 & 643.42\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A tibble: 6 × 2\n", + "A data.frame: 6 × 2\n", "\n", - "| Date <dttm> | trm <dbl> |\n", - "|---|---|\n", - "| 1991-11-27 | 693.32 |\n", - "| 1991-11-28 | 693.99 |\n", - "| 1991-11-29 | 694.70 |\n", - "| 1991-11-30 | 694.70 |\n", - "| 1991-12-01 | 643.42 |\n", - "| 1991-12-02 | 643.42 |\n", + "| | Date <dttm> | trm <dbl> |\n", + "|---|---|---|\n", + "| 1 | 1991-11-27 | 693.32 |\n", + "| 2 | 1991-11-28 | 693.99 |\n", + "| 3 | 1991-11-29 | 694.70 |\n", + "| 4 | 1991-11-30 | 694.70 |\n", + "| 5 | 1991-12-01 | 643.42 |\n", + "| 6 | 1991-12-02 | 643.42 |\n", "\n" ], "text/plain": [ @@ -535,6 +839,7 @@ ], "source": [ "trm <- read_excel(path, sheet='TRM',skip=1, col_names=c('Date','trm'))\n", + "trm <- as.data.frame(trm)\n", "head(trm)" ] }, @@ -542,19 +847,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Simulaciones a futuro" + "## Simulaciones de proyección" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [] } ], "metadata": { From 6ae0c0e13ee674b510285c5050232384e90d08a5 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Sun, 1 May 2022 20:03:43 -0500 Subject: [PATCH 06/10] Punto b. Simulacion tes cop --- Taller 2/Scripts/first.ipynb | 738 +++++++++++++++++++++++++++++------ 1 file changed, 624 insertions(+), 114 deletions(-) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index 0e1ff6a..4146363 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 66, "metadata": { "vscode": { "languageId": "r" @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 67, "metadata": { "vscode": { "languageId": "r" @@ -203,56 +203,16 @@ { "data": { "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 6 × 6 of type dbl
6m1y3y5y10y15y
5.40425.53286.00566.41396.92767.3809
5.37035.51075.99536.42646.94107.3830
5.37495.51766.00446.43126.94307.3839
5.41765.55136.06136.42816.93087.3765
5.43045.55086.04956.40766.88967.3203
5.45705.57366.09116.43636.90427.3267
\n" + "2592" ], "text/latex": [ - "A matrix: 6 × 6 of type dbl\n", - "\\begin{tabular}{llllll}\n", - " 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", - "\\hline\n", - "\t 5.4042 & 5.5328 & 6.0056 & 6.4139 & 6.9276 & 7.3809\\\\\n", - "\t 5.3703 & 5.5107 & 5.9953 & 6.4264 & 6.9410 & 7.3830\\\\\n", - "\t 5.3749 & 5.5176 & 6.0044 & 6.4312 & 6.9430 & 7.3839\\\\\n", - "\t 5.4176 & 5.5513 & 6.0613 & 6.4281 & 6.9308 & 7.3765\\\\\n", - "\t 5.4304 & 5.5508 & 6.0495 & 6.4076 & 6.8896 & 7.3203\\\\\n", - "\t 5.4570 & 5.5736 & 6.0911 & 6.4363 & 6.9042 & 7.3267\\\\\n", - "\\end{tabular}\n" + "2592" ], "text/markdown": [ - "\n", - "A matrix: 6 × 6 of type dbl\n", - "\n", - "| 6m | 1y | 3y | 5y | 10y | 15y |\n", - "|---|---|---|---|---|---|\n", - "| 5.4042 | 5.5328 | 6.0056 | 6.4139 | 6.9276 | 7.3809 |\n", - "| 5.3703 | 5.5107 | 5.9953 | 6.4264 | 6.9410 | 7.3830 |\n", - "| 5.3749 | 5.5176 | 6.0044 | 6.4312 | 6.9430 | 7.3839 |\n", - "| 5.4176 | 5.5513 | 6.0613 | 6.4281 | 6.9308 | 7.3765 |\n", - "| 5.4304 | 5.5508 | 6.0495 | 6.4076 | 6.8896 | 7.3203 |\n", - "| 5.4570 | 5.5736 | 6.0911 | 6.4363 | 6.9042 | 7.3267 |\n", - "\n" + "2592" ], "text/plain": [ - " 6m 1y 3y 5y 10y 15y \n", - "[1,] 5.4042 5.5328 6.0056 6.4139 6.9276 7.3809\n", - "[2,] 5.3703 5.5107 5.9953 6.4264 6.9410 7.3830\n", - "[3,] 5.3749 5.5176 6.0044 6.4312 6.9430 7.3839\n", - "[4,] 5.4176 5.5513 6.0613 6.4281 6.9308 7.3765\n", - "[5,] 5.4304 5.5508 6.0495 6.4076 6.8896 7.3203\n", - "[6,] 5.4570 5.5736 6.0911 6.4363 6.9042 7.3267" + "[1] 2592" ] }, "metadata": {}, @@ -265,82 +225,21 @@ "rownames(tes_cop) <- tes_cop$Date\n", "tes_cop <- tes_cop[,-c(1)]\n", "tes_cop <- sapply(tes_cop, as.numeric)\n", - "\n", - "head(tes_cop)\n" + "\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 121, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 6 × 4 of type dbl
beta_0beta_1beta_2lambda
8.378015-3.115947 0.036157500.1887807
8.308243-3.090228-0.070084140.2109684
8.295119-3.071663-0.041081350.2109684
8.253432-2.979203-0.043223110.2109684
8.161660-2.874677-0.024016360.2109684
8.095042-2.787337-0.025973860.2241687
\n" - ], - "text/latex": [ - "A matrix: 6 × 4 of type dbl\n", - "\\begin{tabular}{llll}\n", - " beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", - "\\hline\n", - "\t 8.378015 & -3.115947 & 0.03615750 & 0.1887807\\\\\n", - "\t 8.308243 & -3.090228 & -0.07008414 & 0.2109684\\\\\n", - "\t 8.295119 & -3.071663 & -0.04108135 & 0.2109684\\\\\n", - "\t 8.253432 & -2.979203 & -0.04322311 & 0.2109684\\\\\n", - "\t 8.161660 & -2.874677 & -0.02401636 & 0.2109684\\\\\n", - "\t 8.095042 & -2.787337 & -0.02597386 & 0.2241687\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 6 × 4 of type dbl\n", - "\n", - "| beta_0 | beta_1 | beta_2 | lambda |\n", - "|---|---|---|---|\n", - "| 8.378015 | -3.115947 | 0.03615750 | 0.1887807 |\n", - "| 8.308243 | -3.090228 | -0.07008414 | 0.2109684 |\n", - "| 8.295119 | -3.071663 | -0.04108135 | 0.2109684 |\n", - "| 8.253432 | -2.979203 | -0.04322311 | 0.2109684 |\n", - "| 8.161660 | -2.874677 | -0.02401636 | 0.2109684 |\n", - "| 8.095042 | -2.787337 | -0.02597386 | 0.2241687 |\n", - "\n" - ], - "text/plain": [ - " beta_0 beta_1 beta_2 lambda \n", - "[1,] 8.378015 -3.115947 0.03615750 0.1887807\n", - "[2,] 8.308243 -3.090228 -0.07008414 0.2109684\n", - "[3,] 8.295119 -3.071663 -0.04108135 0.2109684\n", - "[4,] 8.253432 -2.979203 -0.04322311 0.2109684\n", - "[5,] 8.161660 -2.874677 -0.02401636 0.2109684\n", - "[6,] 8.095042 -2.787337 -0.02597386 0.2241687" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "tes_maturity <- c(0.5,1,3,5,10,15)\n", - "tes_cop_ns <- Nelson.Siegel( rate=tes_cop,\tmaturity=tes_maturity)\n", - "head(tes_cop_ns)" + "tes_cop_ns <- Nelson.Siegel( rate=tes_cop,\tmaturity=tes_maturity)\n" ] }, { @@ -685,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": { "vscode": { "languageId": "r" @@ -769,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": { "vscode": { "languageId": "r" @@ -847,7 +746,618 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Simulaciones de proyección" + "## Simulaciones de proyección\n", + "En esta sección se estiman las 1000 simulaciones para cada uno de los productos financieros. En el caso de los bonos TES cop, TES UVR y corp EEUU se realiza el análisis de componentes principales." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "librerias <- c(\"forecast\",\"xts\",\"rugarch\",\"timeSeries\",\"ggplot2\",\"astsa\",\"scales\",\"lubridate\",\"reshape2\",\"quantmod\",\"xtable\",\"tseries\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "library('stats')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'forecast' was built under R version 3.6.3\"Registered S3 method overwritten by 'quantmod':\n", + " method from\n", + " as.zoo.data.frame zoo \n", + "Warning message:\n", + "\"package 'rugarch' was built under R version 3.6.3\"\n", + "Attaching package: 'rugarch'\n", + "\n", + "The following object is masked from 'package:stats':\n", + "\n", + " sigma\n", + "\n", + "Warning message:\n", + "\"package 'timeSeries' was built under R version 3.6.3\"Warning message:\n", + "\"package 'timeDate' was built under R version 3.6.2\"\n", + "Attaching package: 'timeSeries'\n", + "\n", + "The following object is masked from 'package:zoo':\n", + "\n", + " time<-\n", + "\n", + "\n", + "Attaching package: 'astsa'\n", + "\n", + "The following object is masked from 'package:forecast':\n", + "\n", + " gas\n", + "\n", + "Warning message:\n", + "\"package 'scales' was built under R version 3.6.3\"Warning message:\n", + "\"package 'reshape2' was built under R version 3.6.3\"\n", + "Attaching package: 'reshape2'\n", + "\n", + "The following object is masked from 'package:tidyr':\n", + "\n", + " smiths\n", + "\n", + "Version 0.4-0 included new data defaults. See ?getSymbols.\n", + "Warning message:\n", + "\"package 'xtable' was built under R version 3.6.3\"\n", + "Attaching package: 'xtable'\n", + "\n", + "The following object is masked from 'package:timeSeries':\n", + "\n", + " align\n", + "\n", + "The following object is masked from 'package:timeDate':\n", + "\n", + " align\n", + "\n", + "Warning message:\n", + "\"package 'tseries' was built under R version 3.6.3\"" + ] + } + ], + "source": [ + "if(length(setdiff(librerias, rownames(installed.packages()))) > 0){\n", + " install.packages(setdiff(librerias, rownames(installed.packages())))}\n", + "invisible(sapply(librerias, require, character.only = TRUE,quietly = TRUE))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TES Pesos\n", + "Lo primero es que obtenemos el cambio en los betas y el lambda de NSiegel:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "i_tes_cop <- tes_cop_ns[-nrow(tes_cop_ns),]\n", + "f_tes_cop <- tes_cop_ns[-1,]\n", + "delta_tes_cop_ns <- f_tes_cop - i_tes_cop" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 6 × 4 of type dbl
beta_0beta_1beta_2lambda
-0.06977175 0.02571902-0.106241637 0.02218765
-0.01312430 0.01856500 0.029002789 0.00000000
-0.04168666 0.09245968-0.002141761 0.00000000
-0.09177265 0.10452687 0.019206748 0.00000000
-0.06661783 0.08733963-0.001957500 0.01320032
0.04701532-0.02721950-0.017491214-0.01320032
\n" + ], + "text/latex": [ + "A matrix: 6 × 4 of type dbl\n", + "\\begin{tabular}{llll}\n", + " beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", + "\\hline\n", + "\t -0.06977175 & 0.02571902 & -0.106241637 & 0.02218765\\\\\n", + "\t -0.01312430 & 0.01856500 & 0.029002789 & 0.00000000\\\\\n", + "\t -0.04168666 & 0.09245968 & -0.002141761 & 0.00000000\\\\\n", + "\t -0.09177265 & 0.10452687 & 0.019206748 & 0.00000000\\\\\n", + "\t -0.06661783 & 0.08733963 & -0.001957500 & 0.01320032\\\\\n", + "\t 0.04701532 & -0.02721950 & -0.017491214 & -0.01320032\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 6 × 4 of type dbl\n", + "\n", + "| beta_0 | beta_1 | beta_2 | lambda |\n", + "|---|---|---|---|\n", + "| -0.06977175 | 0.02571902 | -0.106241637 | 0.02218765 |\n", + "| -0.01312430 | 0.01856500 | 0.029002789 | 0.00000000 |\n", + "| -0.04168666 | 0.09245968 | -0.002141761 | 0.00000000 |\n", + "| -0.09177265 | 0.10452687 | 0.019206748 | 0.00000000 |\n", + "| -0.06661783 | 0.08733963 | -0.001957500 | 0.01320032 |\n", + "| 0.04701532 | -0.02721950 | -0.017491214 | -0.01320032 |\n", + "\n" + ], + "text/plain": [ + " beta_0 beta_1 beta_2 lambda \n", + "[1,] -0.06977175 0.02571902 -0.106241637 0.02218765\n", + "[2,] -0.01312430 0.01856500 0.029002789 0.00000000\n", + "[3,] -0.04168666 0.09245968 -0.002141761 0.00000000\n", + "[4,] -0.09177265 0.10452687 0.019206748 0.00000000\n", + "[5,] -0.06661783 0.08733963 -0.001957500 0.01320032\n", + "[6,] 0.04701532 -0.02721950 -0.017491214 -0.01320032" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(delta_tes_cop_ns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Con la metodologìa de PCA reducimos la dimensionalidad:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Importance of components:\n", + " PC1 PC2 PC3 PC4\n", + "Standard deviation 1.6131 1.1051 0.38098 0.17741\n", + "Proportion of Variance 0.6506 0.3053 0.03629 0.00787\n", + "Cumulative Proportion 0.6506 0.9558 0.99213 1.00000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca_tes_cop <- prcomp(delta_tes_cop_ns, scale=TRUE)\n", + "summary(pca_tes_cop)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 6 × 4 of type dbl
PC1PC2PC3PC4
-0.01321555-0.23584992 0.064994517-0.067192525
-0.04888901-0.02182426-0.003923853 0.005501787
-0.15294004-0.12858967-0.085797390 0.026638042
-0.23522372-0.17617426-0.065824742-0.033053438
-0.13905373-0.22124785-0.007551177 0.010983057
0.05181849 0.14164076-0.059745127 0.011150354
\n" + ], + "text/latex": [ + "A matrix: 6 × 4 of type dbl\n", + "\\begin{tabular}{llll}\n", + " PC1 & PC2 & PC3 & PC4\\\\\n", + "\\hline\n", + "\t -0.01321555 & -0.23584992 & 0.064994517 & -0.067192525\\\\\n", + "\t -0.04888901 & -0.02182426 & -0.003923853 & 0.005501787\\\\\n", + "\t -0.15294004 & -0.12858967 & -0.085797390 & 0.026638042\\\\\n", + "\t -0.23522372 & -0.17617426 & -0.065824742 & -0.033053438\\\\\n", + "\t -0.13905373 & -0.22124785 & -0.007551177 & 0.010983057\\\\\n", + "\t 0.05181849 & 0.14164076 & -0.059745127 & 0.011150354\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 6 × 4 of type dbl\n", + "\n", + "| PC1 | PC2 | PC3 | PC4 |\n", + "|---|---|---|---|\n", + "| -0.01321555 | -0.23584992 | 0.064994517 | -0.067192525 |\n", + "| -0.04888901 | -0.02182426 | -0.003923853 | 0.005501787 |\n", + "| -0.15294004 | -0.12858967 | -0.085797390 | 0.026638042 |\n", + "| -0.23522372 | -0.17617426 | -0.065824742 | -0.033053438 |\n", + "| -0.13905373 | -0.22124785 | -0.007551177 | 0.010983057 |\n", + "| 0.05181849 | 0.14164076 | -0.059745127 | 0.011150354 |\n", + "\n" + ], + "text/plain": [ + " PC1 PC2 PC3 PC4 \n", + "[1,] -0.01321555 -0.23584992 0.064994517 -0.067192525\n", + "[2,] -0.04888901 -0.02182426 -0.003923853 0.005501787\n", + "[3,] -0.15294004 -0.12858967 -0.085797390 0.026638042\n", + "[4,] -0.23522372 -0.17617426 -0.065824742 -0.033053438\n", + "[5,] -0.13905373 -0.22124785 -0.007551177 0.010983057\n", + "[6,] 0.05181849 0.14164076 -0.059745127 0.011150354" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(pca_tes_cop$x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seleccionamos los primeros 2 componentes que explican el 95% del comportamiento de la serie y corremos un arima para cada uno de ellos" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_tes_cop$residuals\n", + "X-squared = 0.018308, df = 1, p-value = 0.8924\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_tes_cop$residuals\n", + "X-squared = 0.018308, df = 1, p-value = 0.8924\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_pca_1_tes_cop <- auto.arima(pca_tes_cop$x[,1],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_1_tes_cop$residuals)\n", + "arima_pca_2_tes_cop <- auto.arima(pca_tes_cop$x[,2],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_1_tes_cop$residuals)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulamos a 260 periodos en el futuro (1 año) y realizamos el mismo procedimiento 1000 veces" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "future <- 260 #52 semanas y 5 dìas a la semana para evaluar a un año\n", + "simulations <- 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "sim_pca_1_tes_cop <- replicate(expr = simulate(object = arima_pca_1_tes_cop,nsim = future),n = simulations)\n", + "sim_pca_2_tes_cop <- replicate(expr = simulate(object =arima_pca_2_tes_cop,nsim = future),n = simulations)\n", + "Bt1 <- pca_tes_cop$rotation[,1] #Eigen values del primer componente\n", + "Bt2 <- pca_tes_cop$rotation[,2] #Eigen values del segundo componente\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reversamos el procedimiento de PCA, multiplicando los eigen values por el cambio en cada componente" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A matrix: 3 × 1000 of type dbl
0.8667266-0.6162629-0.7101092-0.02593674-1.4947931 0.1331738 0.2385630-1.3218142-0.2986355-1.3195469...-0.05672787-0.51160508-0.9750071-0.81583947 0.2662714-1.3493055-0.8440075-0.63457252-1.41531198 0.1629867
-0.8934856 0.6276854 0.8532216 0.14676585 0.5528627-0.2451592-0.4553099-0.2240811 0.5991739-0.0370114...-0.90343711-0.03551573 0.3742178 0.23826482-1.1378925 0.5160537 1.1304199-0.14146838 0.28514031-0.2556540
0.1433665-0.8739295 0.3607352-0.56205363 1.7892906 0.4841982 0.1907714 1.0055555-0.6151569-0.1830808... 0.07051410 0.04185852 0.4738914 0.04853035 1.2690662-1.0985671-0.2457784 0.05247762-0.09210574-0.4420530
\n" + ], + "text/latex": [ + "A matrix: 3 × 1000 of type dbl\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + "\t 0.8667266 & -0.6162629 & -0.7101092 & -0.02593674 & -1.4947931 & 0.1331738 & 0.2385630 & -1.3218142 & -0.2986355 & -1.3195469 & ... & -0.05672787 & -0.51160508 & -0.9750071 & -0.81583947 & 0.2662714 & -1.3493055 & -0.8440075 & -0.63457252 & -1.41531198 & 0.1629867\\\\\n", + "\t -0.8934856 & 0.6276854 & 0.8532216 & 0.14676585 & 0.5528627 & -0.2451592 & -0.4553099 & -0.2240811 & 0.5991739 & -0.0370114 & ... & -0.90343711 & -0.03551573 & 0.3742178 & 0.23826482 & -1.1378925 & 0.5160537 & 1.1304199 & -0.14146838 & 0.28514031 & -0.2556540\\\\\n", + "\t 0.1433665 & -0.8739295 & 0.3607352 & -0.56205363 & 1.7892906 & 0.4841982 & 0.1907714 & 1.0055555 & -0.6151569 & -0.1830808 & ... & 0.07051410 & 0.04185852 & 0.4738914 & 0.04853035 & 1.2690662 & -1.0985671 & -0.2457784 & 0.05247762 & -0.09210574 & -0.4420530\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A matrix: 3 × 1000 of type dbl\n", + "\n", + "| 0.8667266 | -0.6162629 | -0.7101092 | -0.02593674 | -1.4947931 | 0.1331738 | 0.2385630 | -1.3218142 | -0.2986355 | -1.3195469 | ... | -0.05672787 | -0.51160508 | -0.9750071 | -0.81583947 | 0.2662714 | -1.3493055 | -0.8440075 | -0.63457252 | -1.41531198 | 0.1629867 |\n", + "| -0.8934856 | 0.6276854 | 0.8532216 | 0.14676585 | 0.5528627 | -0.2451592 | -0.4553099 | -0.2240811 | 0.5991739 | -0.0370114 | ... | -0.90343711 | -0.03551573 | 0.3742178 | 0.23826482 | -1.1378925 | 0.5160537 | 1.1304199 | -0.14146838 | 0.28514031 | -0.2556540 |\n", + "| 0.1433665 | -0.8739295 | 0.3607352 | -0.56205363 | 1.7892906 | 0.4841982 | 0.1907714 | 1.0055555 | -0.6151569 | -0.1830808 | ... | 0.07051410 | 0.04185852 | 0.4738914 | 0.04853035 | 1.2690662 | -1.0985671 | -0.2457784 | 0.05247762 | -0.09210574 | -0.4420530 |\n", + "\n" + ], + "text/plain": [ + " [,1] [,2] [,3] [,4] [,5] [,6] \n", + "[1,] 0.8667266 -0.6162629 -0.7101092 -0.02593674 -1.4947931 0.1331738\n", + "[2,] -0.8934856 0.6276854 0.8532216 0.14676585 0.5528627 -0.2451592\n", + "[3,] 0.1433665 -0.8739295 0.3607352 -0.56205363 1.7892906 0.4841982\n", + " [,7] [,8] [,9] [,10] [,11] [,12] [,13] \n", + "[1,] 0.2385630 -1.3218142 -0.2986355 -1.3195469 ... -0.05672787 -0.51160508\n", + "[2,] -0.4553099 -0.2240811 0.5991739 -0.0370114 ... -0.90343711 -0.03551573\n", + "[3,] 0.1907714 1.0055555 -0.6151569 -0.1830808 ... 0.07051410 0.04185852\n", + " [,14] [,15] [,16] [,17] [,18] [,19] \n", + "[1,] -0.9750071 -0.81583947 0.2662714 -1.3493055 -0.8440075 -0.63457252\n", + "[2,] 0.3742178 0.23826482 -1.1378925 0.5160537 1.1304199 -0.14146838\n", + "[3,] 0.4738914 0.04853035 1.2690662 -1.0985671 -0.2457784 0.05247762\n", + " [,20] [,21] \n", + "[1,] -1.41531198 0.1629867\n", + "[2,] 0.28514031 -0.2556540\n", + "[3,] -0.09210574 -0.4420530" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "delta<-matrix(,nrow=future, ncol=simulations)\n", + "\n", + "for (i in 1:future){\n", + " for (j in 1:simulations){\n", + " delta[i,j] <- sum(sim_pca_1_tes_cop[i,j]*Bt1)+sum(sim_pca_2_tes_cop[i,j]*Bt2)\n", + " }\n", + "}\n", + "head(delta, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "f_ns_tes_cop <- list()\n", + "alpha <- tes_cop_ns[nrow(tes_cop_ns),] \n", + "alphanuevo <- alpha + 2\n", + "for (i in 1:simulations){\n", + " newalph <- alpha\n", + " for (j in 1:future){\n", + " newalph <- newalph + delta[j,i] \n", + " if (j==260){\n", + " f_ns_tes_cop <- append(f_ns_tes_cop, newalph) #Guardamos unicamente los resultados del último periodo pues es la frontera a un año\n", + " }\n", + " }\n", + "}\n", + "mat_f_ns_tes_cop <- matrix(f_ns_tes_cop, ncol=4,byrow=T)\n", + "mat_f_ns_tes_cop <- as.data.frame(mat_f_ns_tes_cop )" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "names(mat_f_ns_tes_cop) <- c('beta_0','beta_1', 'beta_2', 'lambda')" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "mat_f_ns_tes_cop<- data.frame(lapply(mat_f_ns_tes_cop, function(x) as.numeric(x)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculamos las tasas del bono por medio de los parametros de NS" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "nelson_sieguel_rate <- function(insumo,maturity){\n", + " x <- insumo[,1] + insumo[,2]*((1 - exp(-insumo[,4]*maturity))/insumo[,4]*maturity)+insumo[,3]*(((1- exp(-insumo[,4]*maturity))/insumo[,4]*maturity)-exp(-insumo[,4]*maturity))\n", + " x <- as.data.frame(x)\n", + " names(x) <- c('rate')\n", + " return(x)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "tes_f_rates <- nelson_sieguel_rate(mat_f_ns_tes_cop,0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + " rate \n", + " Min. : 11.27 \n", + " 1st Qu.: 11.61 \n", + " Median : 12.95 \n", + " Mean : 24.46 \n", + " 3rd Qu.: 16.57 \n", + " Max. :849.82 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " 6m 1y 3y 5y \n", + " Min. :1.946 Min. :2.137 Min. :3.026 Min. :3.888 \n", + " 1st Qu.:4.136 1st Qu.:4.336 1st Qu.:5.118 1st Qu.:5.607 \n", + " Median :4.637 Median :4.739 Median :5.450 Median :6.040 \n", + " Mean :4.617 Mean :4.759 Mean :5.490 Mean :6.058 \n", + " 3rd Qu.:5.289 3rd Qu.:5.368 3rd Qu.:5.853 3rd Qu.:6.449 \n", + " Max. :7.951 Max. :8.104 Max. :9.205 Max. :9.802 \n", + " 10y 15y \n", + " Min. : 4.811 Min. : 5.212 \n", + " 1st Qu.: 6.430 1st Qu.: 6.850 \n", + " Median : 6.815 Median : 7.221 \n", + " Mean : 6.869 Mean : 7.304 \n", + " 3rd Qu.: 7.248 3rd Qu.: 7.726 \n", + " Max. :10.249 Max. :10.284 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(tes_f_rates)\n", + "summary(tes_cop)" ] } ], From a112508d3fc88c60362756a19a5b2dcd5a161a8e Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Thu, 5 May 2022 14:15:22 -0500 Subject: [PATCH 07/10] =?UTF-8?q?Actualizaci=C3=B3n=20de=20punto=20a.=20Si?= =?UTF-8?q?m=201000=20veces?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Taller 2/Scripts/first.ipynb | 585 +++++++++++++++++++++++++++++++++-- 1 file changed, 566 insertions(+), 19 deletions(-) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index 4146363..38b8443 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 56, "metadata": { "vscode": { "languageId": "r" @@ -22,29 +22,25 @@ "output_type": "stream", "text": [ "Warning message:\n", - "\"package 'readxl' was built under R version 3.6.3\"Warning message:\n", - "\"package 'lubridate' was built under R version 3.6.3\"\n", - "Attaching package: 'lubridate'\n", + "\"package 'dplyr' was built under R version 3.6.3\"\n", + "Attaching package: 'dplyr'\n", "\n", - "The following objects are masked from 'package:base':\n", + "The following objects are masked from 'package:data.table':\n", "\n", - " date, intersect, setdiff, union\n", + " between, first, last\n", "\n", - "Warning message:\n", - "\"package 'tidyr' was built under R version 3.6.3\"Warning message:\n", - "\"package 'YieldCurve' was built under R version 3.6.3\"Loading required package: xts\n", - "Loading required package: zoo\n", - "Warning message:\n", - "\"package 'zoo' was built under R version 3.6.3\"\n", - "Attaching package: 'zoo'\n", + "The following objects are masked from 'package:xts':\n", "\n", - "The following objects are masked from 'package:base':\n", + " first, last\n", + "\n", + "The following objects are masked from 'package:stats':\n", + "\n", + " filter, lag\n", "\n", - " as.Date, as.Date.numeric\n", + "The following objects are masked from 'package:base':\n", "\n", - "Registered S3 method overwritten by 'xts':\n", - " method from\n", - " as.zoo.xts zoo \n" + " intersect, setdiff, setequal, union\n", + "\n" ] } ], @@ -53,7 +49,8 @@ "library(\"lubridate\")\n", "library('tidyr')\n", "library('ggplot2')\n", - "library('YieldCurve')" + "library('YieldCurve')\n", + "library('dplyr')" ] }, { @@ -149,6 +146,556 @@ "source(\"helpers.R\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulación de personas" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "pathmort <- 'C:/Users/Diana C Contreras/OneDrive - Universidad de Los Andes/Riesgo Financiero/Talleres/T1-Riesgo/Taller 2/data/Mortality.xlsx'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 3
ageq_mq_f
<dbl><dbl><dbl>
1500.0033530.001880
2510.0036410.002042
3520.0039560.002219
4530.0043010.002412
5540.0046810.002625
6550.0050500.002833
\n" + ], + "text/latex": [ + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & age & q\\_m & q\\_f\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & 50 & 0.003353 & 0.001880\\\\\n", + "\t2 & 51 & 0.003641 & 0.002042\\\\\n", + "\t3 & 52 & 0.003956 & 0.002219\\\\\n", + "\t4 & 53 & 0.004301 & 0.002412\\\\\n", + "\t5 & 54 & 0.004681 & 0.002625\\\\\n", + "\t6 & 55 & 0.005050 & 0.002833\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 3\n", + "\n", + "| | age <dbl> | q_m <dbl> | q_f <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 50 | 0.003353 | 0.001880 |\n", + "| 2 | 51 | 0.003641 | 0.002042 |\n", + "| 3 | 52 | 0.003956 | 0.002219 |\n", + "| 4 | 53 | 0.004301 | 0.002412 |\n", + "| 5 | 54 | 0.004681 | 0.002625 |\n", + "| 6 | 55 | 0.005050 | 0.002833 |\n", + "\n" + ], + "text/plain": [ + " age q_m q_f \n", + "1 50 0.003353 0.001880\n", + "2 51 0.003641 0.002042\n", + "3 52 0.003956 0.002219\n", + "4 53 0.004301 0.002412\n", + "5 54 0.004681 0.002625\n", + "6 55 0.005050 0.002833" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mortality <- read_excel(pathmort, sheet=1,skip=2, col_names=c('age','i_m','d_m','q_m','e_m','age_2','i_f','d_f','q_f','e_f'))\n", + "mortality <- mortality[,c(1,4,9)]\n", + "mortality <- data.frame(lapply(mortality, function(x) as.numeric(as.character(x))))\n", + "head(mortality)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "simulaciones <- function(future=60){\n", + " t_ages<- list()\n", + " for (i in 50:89){\n", + " m=7500\n", + " f=7500\n", + " t_l <- c(15000)\n", + " for (j in 1:future){\n", + " m <- sum(rbinom(m, size=1, prob= (1-mortality$q_m[j])))\n", + " f <- sum(rbinom(f, size=1, prob= (1-mortality$q_f[j])))\n", + " t_l <- c(t_l, (m+f) )\n", + " }\n", + " t_ages<- c(t_ages, t_l)\n", + " }\n", + " t_ages <- as.data.frame(matrix(t_ages, byrow=TRUE, nrow= length(c(50:89)) ))\n", + " t_ages <- data.frame(lapply(t_ages, function(x) as.numeric(as.character(x))))\n", + " t_sum <- colSums(t_ages)\n", + "return(t_sum) \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "future <- 60\n", + "sims <- data.frame(matrix(NA, nrow = 1000, ncol = (future+1)))\n", + "for (i in 1:1000){\n", + " sims[i,] <- simulaciones(future)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "write.csv(sims,'C:/Users/Diana C Contreras/OneDrive - Universidad de Los Andes/Riesgo Financiero/Talleres/T1-Riesgo/Taller 2/data/proyection.csv',row.names=F)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Installing package into 'C:/Users/Diana C Contreras/Documents/R/win-library/3.6'\n", + "(as 'lib' is unspecified)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " There is a binary version available but the source version is later:\n", + " binary source needs_compilation\n", + "data.table 1.14.0 1.14.2 TRUE\n", + "\n", + " Binaries will be installed\n", + "package 'data.table' successfully unpacked and MD5 sums checked\n", + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\Diana C Contreras\\AppData\\Local\\Temp\\RtmpEnl9rI\\downloaded_packages\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"package 'data.table' was built under R version 3.6.3\"\n", + "Attaching package: 'data.table'\n", + "\n", + "The following objects are masked from 'package:xts':\n", + "\n", + " first, last\n", + "\n", + "The following objects are masked from 'package:lubridate':\n", + "\n", + " hour, isoweek, mday, minute, month, quarter, second, wday, week,\n", + " yday, year\n", + "\n" + ] + } + ], + "source": [ + "install.packages('data.table')\n", + "library('data.table')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "sims$id <- seq(1, 1000, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message in melt(data = sims, id.vars = \"id\", variable.name = \"period_f\", :\n", + "\"The melt generic in data.table has been passed a data.frame and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(sims). In the next version, this warning will become an error.\"" + ] + } + ], + "source": [ + "long_sims <- melt(data= sims,id.vars='id', variable.name=\"period_f\", value.name= \"alive\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 3
idperiod_falive
<dbl><fct><dbl>
11X16e+05
22X16e+05
33X16e+05
44X16e+05
55X16e+05
66X16e+05
\n" + ], + "text/latex": [ + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & id & period\\_f & alive\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & X1 & 6e+05\\\\\n", + "\t2 & 2 & X1 & 6e+05\\\\\n", + "\t3 & 3 & X1 & 6e+05\\\\\n", + "\t4 & 4 & X1 & 6e+05\\\\\n", + "\t5 & 5 & X1 & 6e+05\\\\\n", + "\t6 & 6 & X1 & 6e+05\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 3\n", + "\n", + "| | id <dbl> | period_f <fct> | alive <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 1 | X1 | 6e+05 |\n", + "| 2 | 2 | X1 | 6e+05 |\n", + "| 3 | 3 | X1 | 6e+05 |\n", + "| 4 | 4 | X1 | 6e+05 |\n", + "| 5 | 5 | X1 | 6e+05 |\n", + "| 6 | 6 | X1 | 6e+05 |\n", + "\n" + ], + "text/plain": [ + " id period_f alive\n", + "1 1 X1 6e+05\n", + "2 2 X1 6e+05\n", + "3 3 X1 6e+05\n", + "4 4 X1 6e+05\n", + "5 5 X1 6e+05\n", + "6 6 X1 6e+05" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(long_sims)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "long_sims$period_f <- as.numeric(substring(as.character(long_sims$period_f), 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 3
idperiod_falive
<dbl><dbl><dbl>
1116e+05
2216e+05
3316e+05
4416e+05
5516e+05
6616e+05
\n" + ], + "text/latex": [ + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & id & period\\_f & alive\\\\\n", + " & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & 1 & 6e+05\\\\\n", + "\t2 & 2 & 1 & 6e+05\\\\\n", + "\t3 & 3 & 1 & 6e+05\\\\\n", + "\t4 & 4 & 1 & 6e+05\\\\\n", + "\t5 & 5 & 1 & 6e+05\\\\\n", + "\t6 & 6 & 1 & 6e+05\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 3\n", + "\n", + "| | id <dbl> | period_f <dbl> | alive <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 1 | 1 | 6e+05 |\n", + "| 2 | 2 | 1 | 6e+05 |\n", + "| 3 | 3 | 1 | 6e+05 |\n", + "| 4 | 4 | 1 | 6e+05 |\n", + "| 5 | 5 | 1 | 6e+05 |\n", + "| 6 | 6 | 1 | 6e+05 |\n", + "\n" + ], + "text/plain": [ + " id period_f alive\n", + "1 1 1 6e+05\n", + "2 2 1 6e+05\n", + "3 3 1 6e+05\n", + "4 4 1 6e+05\n", + "5 5 1 6e+05\n", + "6 6 1 6e+05" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(long_sims)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "long_sims$flow <- long_sims$alive*0.000018\n", + "c_l_sims <- long_sims %>%\n", + " group_by(period_f) %>%\n", + " summarise(alive = mean(alive), flow = mean(flow))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(c_l_sims,aes(period_f,alive))+ geom_col( fill='deeppink4') + labs(x=\"años en el futuro\", y='Afiliados vivos',title='Evolución de afiliados vivos') +theme_classic()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "ggplot(c_l_sims,aes(period_f,flow))+ geom_col(fill='chartreuse4') + labs(x=\"años en el futuro\", y='Billones de pesos',title='Flujos anuales de la compañia') +theme_classic()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
idperiod_faliveflow
<dbl><dbl><dbl><dbl>
1116e+0510.8
2216e+0510.8
3316e+0510.8
4416e+0510.8
5516e+0510.8
6616e+0510.8
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & id & period\\_f & alive & flow\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t1 & 1 & 1 & 6e+05 & 10.8\\\\\n", + "\t2 & 2 & 1 & 6e+05 & 10.8\\\\\n", + "\t3 & 3 & 1 & 6e+05 & 10.8\\\\\n", + "\t4 & 4 & 1 & 6e+05 & 10.8\\\\\n", + "\t5 & 5 & 1 & 6e+05 & 10.8\\\\\n", + "\t6 & 6 & 1 & 6e+05 & 10.8\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | id <dbl> | period_f <dbl> | alive <dbl> | flow <dbl> |\n", + "|---|---|---|---|---|\n", + "| 1 | 1 | 1 | 6e+05 | 10.8 |\n", + "| 2 | 2 | 1 | 6e+05 | 10.8 |\n", + "| 3 | 3 | 1 | 6e+05 | 10.8 |\n", + "| 4 | 4 | 1 | 6e+05 | 10.8 |\n", + "| 5 | 5 | 1 | 6e+05 | 10.8 |\n", + "| 6 | 6 | 1 | 6e+05 | 10.8 |\n", + "\n" + ], + "text/plain": [ + " id period_f alive flow\n", + "1 1 1 6e+05 10.8\n", + "2 2 1 6e+05 10.8\n", + "3 3 1 6e+05 10.8\n", + "4 4 1 6e+05 10.8\n", + "5 5 1 6e+05 10.8\n", + "6 6 1 6e+05 10.8" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(long_sims)" + ] + }, { "cell_type": "markdown", "metadata": {}, From bd46dad49b4047d08846c45871e6c7bbc90f7649 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Thu, 5 May 2022 14:54:47 -0500 Subject: [PATCH 08/10] =?UTF-8?q?Punto=20d.=20calibraci=C3=B3n=20de=20la?= =?UTF-8?q?=20curva?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Taller 2/Scripts/first.ipynb | 72 ++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index 38b8443..cb3c41e 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -1906,6 +1906,78 @@ "summary(tes_f_rates)\n", "summary(tes_cop)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calibración de la curva" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "0.424283357506555" + ], + "text/latex": [ + "0.424283357506555" + ], + "text/markdown": [ + "0.424283357506555" + ], + "text/plain": [ + "[1] 0.4242834" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gamma <- log(0.6)/log(0.3)\n", + "gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "0.575716642493445" + ], + "text/latex": [ + "0.575716642493445" + ], + "text/markdown": [ + "0.575716642493445" + ], + "text/plain": [ + "[1] 0.5757166" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "1- gamma" + ] } ], "metadata": { From db59f507d5fc5107124d723715ec9879d7cb44c2 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Thu, 5 May 2022 21:58:47 -0500 Subject: [PATCH 09/10] =?UTF-8?q?Punto=20b=20proyecci=C3=B3n=20mercado=201?= =?UTF-8?q?=20a=C3=B1o?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Taller 2/Scripts/first.ipynb | 1578 ++++++++++++++++++++++++++++++---- 1 file changed, 1390 insertions(+), 188 deletions(-) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index cb3c41e..da4616e 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "# Optimización del Activo\n", "## Import de paquetes y funciones" @@ -10,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 1, "metadata": { "vscode": { "languageId": "r" @@ -22,12 +24,32 @@ "output_type": "stream", "text": [ "Warning message:\n", - "\"package 'dplyr' was built under R version 3.6.3\"\n", - "Attaching package: 'dplyr'\n", + "\"package 'readxl' was built under R version 3.6.3\"Warning message:\n", + "\"package 'lubridate' was built under R version 3.6.3\"\n", + "Attaching package: 'lubridate'\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " date, intersect, setdiff, union\n", "\n", - "The following objects are masked from 'package:data.table':\n", + "Warning message:\n", + "\"package 'tidyr' was built under R version 3.6.3\"Warning message:\n", + "\"package 'YieldCurve' was built under R version 3.6.3\"Loading required package: xts\n", + "Loading required package: zoo\n", + "Warning message:\n", + "\"package 'zoo' was built under R version 3.6.3\"\n", + "Attaching package: 'zoo'\n", + "\n", + "The following objects are masked from 'package:base':\n", "\n", - " between, first, last\n", + " as.Date, as.Date.numeric\n", + "\n", + "Registered S3 method overwritten by 'xts':\n", + " method from\n", + " as.zoo.xts zoo \n", + "Warning message:\n", + "\"package 'dplyr' was built under R version 3.6.3\"\n", + "Attaching package: 'dplyr'\n", "\n", "The following objects are masked from 'package:xts':\n", "\n", @@ -70,11 +92,12 @@ ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\n", - "
  1. '.GlobalEnv'
  2. 'package:YieldCurve'
  3. 'package:xts'
  4. 'package:zoo'
  5. 'package:ggplot2'
  6. 'package:tidyr'
  7. 'package:lubridate'
  8. 'package:readxl'
  9. 'jupyter:irkernel'
  10. 'jupyter:irkernel'
  11. 'package:stats'
  12. 'package:graphics'
  13. 'package:grDevices'
  14. 'package:utils'
  15. 'package:datasets'
  16. 'package:methods'
  17. 'Autoloads'
  18. 'package:base'
\n" + "
  1. '.GlobalEnv'
  2. 'package:dplyr'
  3. 'package:YieldCurve'
  4. 'package:xts'
  5. 'package:zoo'
  6. 'package:ggplot2'
  7. 'package:tidyr'
  8. 'package:lubridate'
  9. 'package:readxl'
  10. 'jupyter:irkernel'
  11. 'jupyter:irkernel'
  12. 'package:stats'
  13. 'package:graphics'
  14. 'package:grDevices'
  15. 'package:utils'
  16. 'package:datasets'
  17. 'package:methods'
  18. 'Autoloads'
  19. 'package:base'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item '.GlobalEnv'\n", + "\\item 'package:dplyr'\n", "\\item 'package:YieldCurve'\n", "\\item 'package:xts'\n", "\\item 'package:zoo'\n", @@ -96,33 +119,35 @@ ], "text/markdown": [ "1. '.GlobalEnv'\n", - "2. 'package:YieldCurve'\n", - "3. 'package:xts'\n", - "4. 'package:zoo'\n", - "5. 'package:ggplot2'\n", - "6. 'package:tidyr'\n", - "7. 'package:lubridate'\n", - "8. 'package:readxl'\n", - "9. 'jupyter:irkernel'\n", + "2. 'package:dplyr'\n", + "3. 'package:YieldCurve'\n", + "4. 'package:xts'\n", + "5. 'package:zoo'\n", + "6. 'package:ggplot2'\n", + "7. 'package:tidyr'\n", + "8. 'package:lubridate'\n", + "9. 'package:readxl'\n", "10. 'jupyter:irkernel'\n", - "11. 'package:stats'\n", - "12. 'package:graphics'\n", - "13. 'package:grDevices'\n", - "14. 'package:utils'\n", - "15. 'package:datasets'\n", - "16. 'package:methods'\n", - "17. 'Autoloads'\n", - "18. 'package:base'\n", + "11. 'jupyter:irkernel'\n", + "12. 'package:stats'\n", + "13. 'package:graphics'\n", + "14. 'package:grDevices'\n", + "15. 'package:utils'\n", + "16. 'package:datasets'\n", + "17. 'package:methods'\n", + "18. 'Autoloads'\n", + "19. 'package:base'\n", "\n", "\n" ], "text/plain": [ - " [1] \".GlobalEnv\" \"package:YieldCurve\" \"package:xts\" \n", - " [4] \"package:zoo\" \"package:ggplot2\" \"package:tidyr\" \n", - " [7] \"package:lubridate\" \"package:readxl\" \"jupyter:irkernel\" \n", - "[10] \"jupyter:irkernel\" \"package:stats\" \"package:graphics\" \n", - "[13] \"package:grDevices\" \"package:utils\" \"package:datasets\" \n", - "[16] \"package:methods\" \"Autoloads\" \"package:base\" " + " [1] \".GlobalEnv\" \"package:dplyr\" \"package:YieldCurve\"\n", + " [4] \"package:xts\" \"package:zoo\" \"package:ggplot2\" \n", + " [7] \"package:tidyr\" \"package:lubridate\" \"package:readxl\" \n", + "[10] \"jupyter:irkernel\" \"jupyter:irkernel\" \"package:stats\" \n", + "[13] \"package:graphics\" \"package:grDevices\" \"package:utils\" \n", + "[16] \"package:datasets\" \"package:methods\" \"Autoloads\" \n", + "[19] \"package:base\" " ] }, "metadata": {}, @@ -148,7 +173,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "### Simulación de personas" ] @@ -245,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 38, "metadata": { "vscode": { "languageId": "r" @@ -259,9 +287,19 @@ " m=7500\n", " f=7500\n", " t_l <- c(15000)\n", + " k <- i-1\n", " for (j in 1:future){\n", - " m <- sum(rbinom(m, size=1, prob= (1-mortality$q_m[j])))\n", - " f <- sum(rbinom(f, size=1, prob= (1-mortality$q_f[j])))\n", + " s <- k+j\n", + " if (s<110){\n", + " p_m <- mortality[mortality$age==s,2]\n", + " p_f <- mortality[mortality$age==s,3]\n", + " }\n", + " else {\n", + " p_m <- 1\n", + " p_f <- 1\n", + " }\n", + " m <- sum(rbinom(m, size=1, prob= (1-p_m)))\n", + " f <- sum(rbinom(f, size=1, prob= (1-p_f)))\n", " t_l <- c(t_l, (m+f) )\n", " }\n", " t_ages<- c(t_ages, t_l)\n", @@ -275,7 +313,35 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "0.003641" + ], + "text/latex": [ + "0.003641" + ], + "text/markdown": [ + "0.003641" + ], + "text/plain": [ + "[1] 0.003641" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mortality[mortality$age==51,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, "metadata": { "vscode": { "languageId": "r" @@ -283,7 +349,7 @@ }, "outputs": [], "source": [ - "future <- 60\n", + "future <- 30\n", "sims <- data.frame(matrix(NA, nrow = 1000, ncol = (future+1)))\n", "for (i in 1:1000){\n", " sims[i,] <- simulaciones(future)\n", @@ -292,7 +358,359 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 1000 × 31
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\n" + ], + "text/latex": [ + "A data.frame: 1000 × 31\n", + "\\begin{tabular}{lllllllllllllllllllll}\n", + " X1 & X2 & X3 & X4 & X5 & X6 & X7 & X8 & X9 & X10 & ... & X22 & X23 & X24 & X25 & X26 & X27 & X28 & X29 & X30 & X31\\\\\n", + " & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t 6e+05 & 583656 & 566680 & 548964 & 530915 & 512597 & 493894 & 474947 & 455990 & 436708 & ... & 228000 & 213406 & 199373 & 185737 & 172633 & 159779 & 147440 & 135248 & 123804 & 112810\\\\\n", + "\t 6e+05 & 583664 & 566734 & 549299 & 531424 & 513162 & 494365 & 475349 & 456091 & 436898 & ... & 228122 & 213550 & 199456 & 185907 & 172522 & 159830 & 147355 & 135323 & 123820 & 112814\\\\\n", + "\t 6e+05 & 583829 & 566945 & 549234 & 531058 & 512716 & 493941 & 474953 & 455312 & 436267 & ... & 227943 & 213285 & 199241 & 185779 & 172910 & 160110 & 147695 & 135871 & 124316 & 113171\\\\\n", + "\t 6e+05 & 583726 & 566924 & 549303 & 531231 & 512730 & 493955 & 474648 & 455380 & 436024 & ... & 228097 & 213559 & 199544 & 185868 & 172730 & 159689 & 147471 & 135454 & 123774 & 112592\\\\\n", + "\t 6e+05 & 583794 & 566854 & 549166 & 531206 & 512760 & 494106 & 475218 & 456113 & 436858 & ... & 228412 & 213876 & 199888 & 186192 & 172947 & 159961 & 147483 & 135651 & 124207 & 112928\\\\\n", + "\t 6e+05 & 583761 & 566678 & 549167 & 531082 & 512697 & 493904 & 474781 & 455759 & 436455 & ... & 228414 & 213694 & 199652 & 185940 & 172670 & 159762 & 147429 & 135374 & 123968 & 112955\\\\\n", + "\t 6e+05 & 583717 & 566684 & 549086 & 531014 & 512460 & 493555 & 474478 & 455264 & 436145 & ... & 228011 & 213460 & 199342 & 185665 & 172364 & 159424 & 147083 & 134998 & 123725 & 112632\\\\\n", + "\t 6e+05 & 583434 & 566508 & 549068 & 530946 & 512551 & 493693 & 474653 & 455396 & 436103 & ... & 228444 & 214050 & 199977 & 186231 & 172899 & 160041 & 147551 & 135563 & 123882 & 112748\\\\\n", + "\t 6e+05 & 583987 & 567037 & 549456 & 530985 & 512656 & 493989 & 474644 & 455234 & 435976 & ... & 228371 & 213761 & 199636 & 186076 & 172794 & 160005 & 147627 & 135732 & 124149 & 113068\\\\\n", + "\t 6e+05 & 583599 & 566678 & 549169 & 531106 & 512625 & 493809 & 474778 & 455633 & 436408 & ... & 228041 & 213616 & 199598 & 185721 & 172572 & 159797 & 147413 & 135425 & 123797 & 112833\\\\\n", + "\t 6e+05 & 583737 & 566987 & 549507 & 531420 & 512941 & 494290 & 475460 & 456262 & 436678 & ... & 228593 & 213964 & 199968 & 186381 & 172960 & 160199 & 147768 & 135758 & 124218 & 113156\\\\\n", + "\t 6e+05 & 583600 & 566561 & 549124 & 531133 & 512845 & 494037 & 474917 & 455484 & 436400 & ... & 228009 & 213474 & 199465 & 185867 & 172621 & 160054 & 147737 & 135785 & 124293 & 113064\\\\\n", + "\t 6e+05 & 583731 & 566805 & 549197 & 531285 & 513000 & 494268 & 475306 & 455893 & 436502 & ... & 228132 & 213411 & 199284 & 185555 & 172290 & 159461 & 147077 & 135000 & 123508 & 112589\\\\\n", + "\t 6e+05 & 583673 & 566667 & 548988 & 530972 & 512579 & 493604 & 474651 & 455742 & 436606 & ... & 228580 & 213860 & 199808 & 186057 & 172863 & 159807 & 147351 & 135170 & 123686 & 112675\\\\\n", + "\t 6e+05 & 583554 & 566694 & 549252 & 531294 & 513094 & 494318 & 475241 & 456002 & 436820 & ... & 228244 & 213528 & 199396 & 185639 & 172446 & 159563 & 147328 & 135378 & 123931 & 112956\\\\\n", + "\t 6e+05 & 583614 & 566593 & 548842 & 531034 & 512508 & 493793 & 475088 & 455868 & 436661 & ... & 228259 & 213727 & 199647 & 186268 & 172906 & 160052 & 147753 & 135854 & 124358 & 113284\\\\\n", + "\t 6e+05 & 583631 & 566651 & 549108 & 531005 & 512492 & 494049 & 474793 & 455495 & 436381 & ... & 228557 & 214019 & 199877 & 186175 & 172812 & 159879 & 147544 & 135570 & 124112 & 113025\\\\\n", + "\t 6e+05 & 583561 & 566492 & 548963 & 530958 & 512584 & 493812 & 474765 & 455635 & 436325 & ... & 227994 & 213449 & 199215 & 185868 & 172746 & 159781 & 147327 & 135461 & 123721 & 112876\\\\\n", + "\t 6e+05 & 583688 & 566812 & 549267 & 531305 & 512878 & 494296 & 475076 & 455835 & 436823 & ... & 227896 & 213494 & 199453 & 185704 & 172473 & 159649 & 147270 & 135302 & 123747 & 112649\\\\\n", + "\t 6e+05 & 583715 & 567024 & 549360 & 531095 & 512525 & 493956 & 474846 & 455762 & 436466 & ... & 228389 & 213691 & 199505 & 185690 & 172233 & 159358 & 147235 & 135194 & 123756 & 112768\\\\\n", + "\t 6e+05 & 583755 & 566900 & 549263 & 531406 & 513009 & 494231 & 475254 & 455602 & 436251 & ... & 228487 & 213908 & 199761 & 185953 & 172831 & 160076 & 147748 & 135788 & 124359 & 113222\\\\\n", + "\t 6e+05 & 583678 & 566930 & 549210 & 531330 & 512772 & 493983 & 475074 & 455580 & 436345 & ... & 228095 & 213633 & 199419 & 185786 & 172479 & 159583 & 147162 & 135296 & 123850 & 112754\\\\\n", + "\t 6e+05 & 583720 & 566616 & 549287 & 531303 & 512700 & 493901 & 474941 & 455715 & 436555 & ... & 228601 & 214077 & 199731 & 186019 & 172740 & 160199 & 147681 & 135811 & 124314 & 113173\\\\\n", + "\t 6e+05 & 583803 & 566839 & 549314 & 531472 & 513228 & 494210 & 474951 & 455620 & 436440 & ... & 228163 & 213591 & 199816 & 185956 & 172549 & 159750 & 147575 & 135469 & 123822 & 112707\\\\\n", + "\t 6e+05 & 583739 & 567035 & 549602 & 531419 & 513187 & 494457 & 475463 & 456375 & 437059 & ... & 228324 & 213812 & 199620 & 186196 & 172948 & 160114 & 147590 & 135719 & 124151 & 112861\\\\\n", + "\t 6e+05 & 583839 & 567065 & 549423 & 531452 & 513030 & 494111 & 475162 & 455879 & 436513 & ... & 228168 & 213737 & 199714 & 185992 & 172669 & 159864 & 147506 & 135590 & 123975 & 113006\\\\\n", + "\t 6e+05 & 583661 & 566671 & 549093 & 531127 & 512909 & 493845 & 475205 & 455895 & 436413 & ... & 228121 & 213697 & 199629 & 185889 & 172631 & 159800 & 147254 & 135229 & 123921 & 112863\\\\\n", + "\t 6e+05 & 583708 & 566768 & 549150 & 531345 & 512827 & 493878 & 474676 & 455506 & 436096 & ... & 228482 & 213968 & 199905 & 186319 & 172898 & 160119 & 147863 & 136007 & 124593 & 113587\\\\\n", + "\t 6e+05 & 583709 & 566718 & 549098 & 531222 & 512817 & 494003 & 474976 & 455778 & 436876 & ... & 228480 & 213977 & 199872 & 186323 & 172818 & 159718 & 147313 & 135302 & 123930 & 112985\\\\\n", + "\t 6e+05 & 583817 & 567004 & 549340 & 531255 & 512803 & 494059 & 475114 & 455813 & 436546 & ... & 228434 & 213861 & 199879 & 186249 & 173077 & 160364 & 147806 & 135708 & 124202 & 113104\\\\\n", + "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n", + "\t 6e+05 & 583741 & 566768 & 549394 & 531354 & 513012 & 494100 & 475127 & 455931 & 436842 & ... & 228536 & 213980 & 199904 & 186235 & 172998 & 159958 & 147609 & 135615 & 124035 & 112960\\\\\n", + "\t 6e+05 & 583676 & 566503 & 548960 & 531008 & 512911 & 494222 & 475078 & 455666 & 436585 & ... & 228276 & 213607 & 199638 & 185970 & 172350 & 159590 & 147156 & 135206 & 123601 & 112658\\\\\n", + "\t 6e+05 & 583703 & 566883 & 549403 & 531489 & 513140 & 494344 & 475180 & 455901 & 436626 & ... & 228547 & 214084 & 199954 & 186375 & 173119 & 160307 & 148035 & 136166 & 124475 & 113310\\\\\n", + "\t 6e+05 & 583786 & 566711 & 549036 & 531107 & 512759 & 494070 & 475279 & 455904 & 436652 & ... & 227880 & 213586 & 199660 & 186001 & 172849 & 159948 & 147419 & 135438 & 123816 & 112749\\\\\n", + "\t 6e+05 & 583694 & 566575 & 549119 & 531284 & 512898 & 494052 & 475040 & 455700 & 436573 & ... & 228623 & 214114 & 199981 & 186280 & 173180 & 160179 & 147831 & 135976 & 124303 & 113275\\\\\n", + "\t 6e+05 & 583696 & 566617 & 549185 & 531122 & 512976 & 494300 & 475242 & 455985 & 436616 & ... & 228238 & 213899 & 199945 & 186234 & 172908 & 160048 & 147713 & 135783 & 124069 & 112991\\\\\n", + "\t 6e+05 & 583763 & 566667 & 549079 & 531286 & 512855 & 493879 & 474858 & 455640 & 436371 & ... & 228617 & 213920 & 199809 & 186024 & 172581 & 159758 & 147336 & 135317 & 123748 & 112723\\\\\n", + "\t 6e+05 & 583692 & 566823 & 549184 & 531294 & 512997 & 494222 & 475146 & 455817 & 436562 & ... & 228840 & 214336 & 200137 & 186191 & 173045 & 160151 & 147810 & 135725 & 124126 & 113058\\\\\n", + "\t 6e+05 & 583620 & 566694 & 549255 & 531235 & 512868 & 494031 & 474991 & 455761 & 436480 & ... & 227917 & 213242 & 198986 & 185071 & 172006 & 159137 & 146727 & 134679 & 123243 & 112283\\\\\n", + "\t 6e+05 & 583600 & 566650 & 549022 & 531020 & 512747 & 494072 & 474966 & 455299 & 436009 & ... & 228064 & 213373 & 199302 & 185749 & 172550 & 159738 & 147153 & 135211 & 123871 & 112714\\\\\n", + "\t 6e+05 & 583720 & 566618 & 549115 & 531007 & 512545 & 493824 & 474905 & 455763 & 436622 & ... & 228143 & 213705 & 199623 & 185949 & 172579 & 159692 & 147246 & 135242 & 123680 & 112348\\\\\n", + "\t 6e+05 & 583794 & 566772 & 549219 & 531091 & 512784 & 494040 & 475121 & 456023 & 436844 & ... & 228507 & 214013 & 200006 & 186240 & 172762 & 159920 & 147315 & 135296 & 123597 & 112571\\\\\n", + "\t 6e+05 & 583788 & 566771 & 549370 & 531340 & 512885 & 493869 & 474798 & 455496 & 436175 & ... & 228279 & 213676 & 199673 & 185748 & 172598 & 159801 & 147489 & 135475 & 124072 & 113075\\\\\n", + "\t 6e+05 & 583645 & 566790 & 549226 & 531424 & 512973 & 494136 & 475077 & 455815 & 436548 & ... & 228254 & 213898 & 199563 & 185662 & 172598 & 159843 & 147385 & 135500 & 124159 & 113136\\\\\n", + "\t 6e+05 & 583326 & 566221 & 548824 & 530793 & 512267 & 493752 & 474982 & 455535 & 436523 & ... & 228476 & 213835 & 199687 & 186071 & 172587 & 159607 & 147117 & 135051 & 123682 & 112663\\\\\n", + "\t 6e+05 & 583800 & 567073 & 549413 & 531321 & 512645 & 494129 & 474921 & 455857 & 436504 & ... & 228357 & 213828 & 199727 & 186057 & 172764 & 160047 & 147620 & 135794 & 124396 & 113189\\\\\n", + "\t 6e+05 & 583858 & 566806 & 549186 & 531039 & 512673 & 493893 & 475065 & 455729 & 436710 & ... & 228451 & 214061 & 199746 & 185994 & 172666 & 159901 & 147678 & 135754 & 124237 & 113231\\\\\n", + "\t 6e+05 & 583560 & 566657 & 549186 & 531182 & 512513 & 493463 & 474655 & 455413 & 436088 & ... & 228084 & 213740 & 199569 & 185967 & 172727 & 159966 & 147613 & 135753 & 124413 & 113501\\\\\n", + "\t 6e+05 & 583402 & 566491 & 548875 & 530944 & 512611 & 494111 & 475326 & 456040 & 437015 & ... & 228270 & 213643 & 199320 & 185659 & 172545 & 159838 & 147638 & 135854 & 124182 & 113072\\\\\n", + "\t 6e+05 & 583614 & 566478 & 549045 & 531146 & 512632 & 493743 & 474796 & 455710 & 436382 & ... & 228268 & 213594 & 199652 & 185777 & 172530 & 159688 & 147325 & 135498 & 123898 & 112994\\\\\n", + "\t 6e+05 & 583653 & 566747 & 549419 & 531441 & 512877 & 493817 & 474744 & 455829 & 436448 & ... & 228387 & 214017 & 199818 & 186078 & 173047 & 160300 & 147962 & 135802 & 124183 & 113257\\\\\n", + "\t 6e+05 & 583756 & 566740 & 549332 & 531437 & 512939 & 494093 & 475213 & 456192 & 437006 & ... & 228384 & 213604 & 199170 & 185479 & 172280 & 159397 & 146983 & 135177 & 123689 & 112935\\\\\n", + "\t 6e+05 & 583640 & 566613 & 549044 & 531087 & 512761 & 494052 & 475115 & 455961 & 436798 & ... & 228703 & 214212 & 200057 & 186354 & 173155 & 160334 & 147877 & 135867 & 124357 & 113372\\\\\n", + "\t 6e+05 & 583799 & 566583 & 549172 & 530969 & 512430 & 494025 & 474886 & 455809 & 436391 & ... & 228290 & 213610 & 199534 & 185855 & 172635 & 159811 & 147390 & 135569 & 123936 & 112903\\\\\n", + "\t 6e+05 & 583626 & 566613 & 549274 & 531268 & 512739 & 493993 & 474833 & 455500 & 436095 & ... & 227625 & 213192 & 199159 & 185624 & 172306 & 159666 & 147162 & 135167 & 123770 & 112588\\\\\n", + "\t 6e+05 & 583628 & 566573 & 549160 & 531578 & 513230 & 494200 & 475232 & 456157 & 437113 & ... & 229292 & 214761 & 200521 & 186677 & 173314 & 160321 & 147841 & 135853 & 124254 & 113268\\\\\n", + "\t 6e+05 & 583734 & 566777 & 549316 & 531252 & 513098 & 494293 & 474890 & 455621 & 436201 & ... & 228806 & 214304 & 200115 & 186402 & 173103 & 160210 & 147777 & 135728 & 124244 & 113184\\\\\n", + "\t 6e+05 & 583553 & 566605 & 549111 & 530857 & 512463 & 493687 & 474678 & 455562 & 436199 & ... & 228694 & 214215 & 199931 & 186239 & 172972 & 160178 & 147720 & 135820 & 124347 & 113268\\\\\n", + "\t 6e+05 & 583954 & 567136 & 549582 & 531475 & 513074 & 494378 & 475226 & 456128 & 436976 & ... & 229110 & 214639 & 200538 & 186508 & 173262 & 160469 & 148025 & 135897 & 124471 & 113464\\\\\n", + "\t 6e+05 & 583620 & 566804 & 549239 & 531321 & 512720 & 493821 & 474900 & 456018 & 437061 & ... & 228312 & 213810 & 199687 & 185917 & 172914 & 160141 & 147597 & 135614 & 124002 & 113074\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 1000 × 31\n", + "\n", + "| X1 <dbl> | X2 <dbl> | X3 <dbl> | X4 <dbl> | X5 <dbl> | X6 <dbl> | X7 <dbl> | X8 <dbl> | X9 <dbl> | X10 <dbl> | ... ... | X22 <dbl> | X23 <dbl> | X24 <dbl> | X25 <dbl> | X26 <dbl> | X27 <dbl> | X28 <dbl> | X29 <dbl> | X30 <dbl> | X31 <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 6e+05 | 583656 | 566680 | 548964 | 530915 | 512597 | 493894 | 474947 | 455990 | 436708 | ... | 228000 | 213406 | 199373 | 185737 | 172633 | 159779 | 147440 | 135248 | 123804 | 112810 |\n", + "| 6e+05 | 583664 | 566734 | 549299 | 531424 | 513162 | 494365 | 475349 | 456091 | 436898 | ... | 228122 | 213550 | 199456 | 185907 | 172522 | 159830 | 147355 | 135323 | 123820 | 112814 |\n", + "| 6e+05 | 583829 | 566945 | 549234 | 531058 | 512716 | 493941 | 474953 | 455312 | 436267 | ... | 227943 | 213285 | 199241 | 185779 | 172910 | 160110 | 147695 | 135871 | 124316 | 113171 |\n", + "| 6e+05 | 583726 | 566924 | 549303 | 531231 | 512730 | 493955 | 474648 | 455380 | 436024 | ... | 228097 | 213559 | 199544 | 185868 | 172730 | 159689 | 147471 | 135454 | 123774 | 112592 |\n", + "| 6e+05 | 583794 | 566854 | 549166 | 531206 | 512760 | 494106 | 475218 | 456113 | 436858 | ... | 228412 | 213876 | 199888 | 186192 | 172947 | 159961 | 147483 | 135651 | 124207 | 112928 |\n", + "| 6e+05 | 583761 | 566678 | 549167 | 531082 | 512697 | 493904 | 474781 | 455759 | 436455 | ... | 228414 | 213694 | 199652 | 185940 | 172670 | 159762 | 147429 | 135374 | 123968 | 112955 |\n", + "| 6e+05 | 583717 | 566684 | 549086 | 531014 | 512460 | 493555 | 474478 | 455264 | 436145 | ... | 228011 | 213460 | 199342 | 185665 | 172364 | 159424 | 147083 | 134998 | 123725 | 112632 |\n", + "| 6e+05 | 583434 | 566508 | 549068 | 530946 | 512551 | 493693 | 474653 | 455396 | 436103 | ... | 228444 | 214050 | 199977 | 186231 | 172899 | 160041 | 147551 | 135563 | 123882 | 112748 |\n", + "| 6e+05 | 583987 | 567037 | 549456 | 530985 | 512656 | 493989 | 474644 | 455234 | 435976 | ... | 228371 | 213761 | 199636 | 186076 | 172794 | 160005 | 147627 | 135732 | 124149 | 113068 |\n", + "| 6e+05 | 583599 | 566678 | 549169 | 531106 | 512625 | 493809 | 474778 | 455633 | 436408 | ... | 228041 | 213616 | 199598 | 185721 | 172572 | 159797 | 147413 | 135425 | 123797 | 112833 |\n", + "| 6e+05 | 583737 | 566987 | 549507 | 531420 | 512941 | 494290 | 475460 | 456262 | 436678 | ... | 228593 | 213964 | 199968 | 186381 | 172960 | 160199 | 147768 | 135758 | 124218 | 113156 |\n", + "| 6e+05 | 583600 | 566561 | 549124 | 531133 | 512845 | 494037 | 474917 | 455484 | 436400 | ... | 228009 | 213474 | 199465 | 185867 | 172621 | 160054 | 147737 | 135785 | 124293 | 113064 |\n", + "| 6e+05 | 583731 | 566805 | 549197 | 531285 | 513000 | 494268 | 475306 | 455893 | 436502 | ... | 228132 | 213411 | 199284 | 185555 | 172290 | 159461 | 147077 | 135000 | 123508 | 112589 |\n", + "| 6e+05 | 583673 | 566667 | 548988 | 530972 | 512579 | 493604 | 474651 | 455742 | 436606 | ... | 228580 | 213860 | 199808 | 186057 | 172863 | 159807 | 147351 | 135170 | 123686 | 112675 |\n", + "| 6e+05 | 583554 | 566694 | 549252 | 531294 | 513094 | 494318 | 475241 | 456002 | 436820 | ... | 228244 | 213528 | 199396 | 185639 | 172446 | 159563 | 147328 | 135378 | 123931 | 112956 |\n", + "| 6e+05 | 583614 | 566593 | 548842 | 531034 | 512508 | 493793 | 475088 | 455868 | 436661 | ... | 228259 | 213727 | 199647 | 186268 | 172906 | 160052 | 147753 | 135854 | 124358 | 113284 |\n", + "| 6e+05 | 583631 | 566651 | 549108 | 531005 | 512492 | 494049 | 474793 | 455495 | 436381 | ... | 228557 | 214019 | 199877 | 186175 | 172812 | 159879 | 147544 | 135570 | 124112 | 113025 |\n", + "| 6e+05 | 583561 | 566492 | 548963 | 530958 | 512584 | 493812 | 474765 | 455635 | 436325 | ... | 227994 | 213449 | 199215 | 185868 | 172746 | 159781 | 147327 | 135461 | 123721 | 112876 |\n", + "| 6e+05 | 583688 | 566812 | 549267 | 531305 | 512878 | 494296 | 475076 | 455835 | 436823 | ... | 227896 | 213494 | 199453 | 185704 | 172473 | 159649 | 147270 | 135302 | 123747 | 112649 |\n", + "| 6e+05 | 583715 | 567024 | 549360 | 531095 | 512525 | 493956 | 474846 | 455762 | 436466 | ... | 228389 | 213691 | 199505 | 185690 | 172233 | 159358 | 147235 | 135194 | 123756 | 112768 |\n", + "| 6e+05 | 583755 | 566900 | 549263 | 531406 | 513009 | 494231 | 475254 | 455602 | 436251 | ... | 228487 | 213908 | 199761 | 185953 | 172831 | 160076 | 147748 | 135788 | 124359 | 113222 |\n", + "| 6e+05 | 583678 | 566930 | 549210 | 531330 | 512772 | 493983 | 475074 | 455580 | 436345 | ... | 228095 | 213633 | 199419 | 185786 | 172479 | 159583 | 147162 | 135296 | 123850 | 112754 |\n", + "| 6e+05 | 583720 | 566616 | 549287 | 531303 | 512700 | 493901 | 474941 | 455715 | 436555 | ... | 228601 | 214077 | 199731 | 186019 | 172740 | 160199 | 147681 | 135811 | 124314 | 113173 |\n", + "| 6e+05 | 583803 | 566839 | 549314 | 531472 | 513228 | 494210 | 474951 | 455620 | 436440 | ... | 228163 | 213591 | 199816 | 185956 | 172549 | 159750 | 147575 | 135469 | 123822 | 112707 |\n", + "| 6e+05 | 583739 | 567035 | 549602 | 531419 | 513187 | 494457 | 475463 | 456375 | 437059 | ... | 228324 | 213812 | 199620 | 186196 | 172948 | 160114 | 147590 | 135719 | 124151 | 112861 |\n", + "| 6e+05 | 583839 | 567065 | 549423 | 531452 | 513030 | 494111 | 475162 | 455879 | 436513 | ... | 228168 | 213737 | 199714 | 185992 | 172669 | 159864 | 147506 | 135590 | 123975 | 113006 |\n", + "| 6e+05 | 583661 | 566671 | 549093 | 531127 | 512909 | 493845 | 475205 | 455895 | 436413 | ... | 228121 | 213697 | 199629 | 185889 | 172631 | 159800 | 147254 | 135229 | 123921 | 112863 |\n", + "| 6e+05 | 583708 | 566768 | 549150 | 531345 | 512827 | 493878 | 474676 | 455506 | 436096 | ... | 228482 | 213968 | 199905 | 186319 | 172898 | 160119 | 147863 | 136007 | 124593 | 113587 |\n", + "| 6e+05 | 583709 | 566718 | 549098 | 531222 | 512817 | 494003 | 474976 | 455778 | 436876 | ... | 228480 | 213977 | 199872 | 186323 | 172818 | 159718 | 147313 | 135302 | 123930 | 112985 |\n", + "| 6e+05 | 583817 | 567004 | 549340 | 531255 | 512803 | 494059 | 475114 | 455813 | 436546 | ... | 228434 | 213861 | 199879 | 186249 | 173077 | 160364 | 147806 | 135708 | 124202 | 113104 |\n", + "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n", + "| 6e+05 | 583741 | 566768 | 549394 | 531354 | 513012 | 494100 | 475127 | 455931 | 436842 | ... | 228536 | 213980 | 199904 | 186235 | 172998 | 159958 | 147609 | 135615 | 124035 | 112960 |\n", + "| 6e+05 | 583676 | 566503 | 548960 | 531008 | 512911 | 494222 | 475078 | 455666 | 436585 | ... | 228276 | 213607 | 199638 | 185970 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into 'C:/Users/Diana C Contreras/Documents/R/win-library/3.6'\n", - "(as 'lib' is unspecified)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " There is a binary version available but the source version is later:\n", - " binary source needs_compilation\n", - "data.table 1.14.0 1.14.2 TRUE\n", - "\n", - " Binaries will be installed\n", - "package 'data.table' successfully unpacked and MD5 sums checked\n", - "\n", - "The downloaded binary packages are in\n", - "\tC:\\Users\\Diana C Contreras\\AppData\\Local\\Temp\\RtmpEnl9rI\\downloaded_packages\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "\"package 'data.table' was built under R version 3.6.3\"\n", - "Attaching package: 'data.table'\n", - "\n", - "The following objects are masked from 'package:xts':\n", - "\n", - " first, last\n", - "\n", - "The following objects are masked from 'package:lubridate':\n", - "\n", - " hour, isoweek, mday, minute, month, quarter, second, wday, week,\n", - " yday, year\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "install.packages('data.table')\n", + "# install.packages('data.table')\n", "library('data.table')" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 43, "metadata": { "vscode": { "languageId": "r" @@ -376,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 44, "metadata": { "vscode": { "languageId": "r" @@ -398,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 45, "metadata": { "vscode": { "languageId": "r" @@ -472,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 46, "metadata": { "vscode": { "languageId": "r" @@ -485,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": { "vscode": { "languageId": "r" @@ -559,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 47, "metadata": { "vscode": { "languageId": "r" @@ -575,7 +949,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 48, "metadata": { "vscode": { "languageId": "r" @@ -584,7 +958,7 @@ "outputs": [ { "data": { - "image/png": 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", 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pQUkUAzmPtNWJ8U2q/DxmjBEXKYqpQU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\nphU2wnLuE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"text/plain": [ "plot without title" ] @@ -599,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 49, "metadata": { "vscode": { "languageId": "r" @@ -608,7 +982,7 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "plot without title" ] @@ -622,6 +996,24 @@ "ggplot(c_l_sims,aes(period_f,flow))+ geom_col(fill='chartreuse4') + labs(x=\"años en el futuro\", y='Billones de pesos',title='Flujos anuales de la compañia') +theme_classic()" ] }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in parse(text = x, srcfile = src): :1:8: unexpected symbol\n1: !conda install\n ^\n", + "output_type": "error", + "traceback": [ + "Error in parse(text = x, srcfile = src): :1:8: unexpected symbol\n1: !conda install\n ^\nTraceback:\n" + ] + } + ], + "source": [ + "!conda install jupyterthemes" + ] + }, { "cell_type": "code", "execution_count": 49, @@ -707,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "vscode": { "languageId": "r" @@ -727,7 +1119,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 3, "metadata": { "vscode": { "languageId": "r" @@ -740,32 +1132,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 4, "metadata": { "vscode": { "languageId": "r" } }, - "outputs": [ - { - "data": { - "text/html": [ - "2592" - ], - "text/latex": [ - "2592" - ], - "text/markdown": [ - "2592" - ], - "text/plain": [ - "[1] 2592" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "tes_cop <- tes_cop[tes_cop$Date >= \"2012-04-26\",]\n", "tes_cop <- as.data.frame(tes_cop)\n", @@ -777,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 5, "metadata": { "vscode": { "languageId": "r" @@ -799,7 +1172,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "vscode": { "languageId": "r" @@ -884,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 30, "metadata": { "vscode": { "languageId": "r" @@ -963,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "metadata": { "vscode": { "languageId": "r" @@ -1033,7 +1406,7 @@ ], "source": [ "corp_usa_maturity <- c(3/12,0.5,1,3,5,10,15)\n", - "corp_usa_ns <- Nelson.Siegel( rate=corp_usa,\tmaturity=corp_usa_maturity)\n", + "corp_usa_ns <- Nelson.Siegel( rate=corp_usa, maturity=corp_usa_maturity)\n", "head(corp_usa_ns)" ] }, @@ -1046,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 32, "metadata": { "vscode": { "languageId": "r" @@ -1131,7 +1504,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "metadata": { "vscode": { "languageId": "r" @@ -1215,7 +1588,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 34, "metadata": { "vscode": { "languageId": "r" @@ -1293,13 +1666,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Simulaciones de proyección\n", + "# Simulaciones de proyección\n", "En esta sección se estiman las 1000 simulaciones para cada uno de los productos financieros. En el caso de los bonos TES cop, TES UVR y corp EEUU se realiza el análisis de componentes principales." ] }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Set up " + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "vscode": { "languageId": "r" @@ -1312,7 +1695,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "vscode": { "languageId": "r" @@ -1325,7 +1708,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "vscode": { "languageId": "r" @@ -1397,11 +1780,28 @@ "invisible(sapply(librerias, require, character.only = TRUE,quietly = TRUE))" ] }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "future <- 260 #52 semanas y 5 dìas a la semana para evaluar a un año\n", + "simulations <- 1000" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "### TES Pesos\n", + "## TES Pesos\n", "Lo primero es que obtenemos el cambio en los betas y el lambda de NSiegel:" ] }, @@ -1656,20 +2056,6 @@ "Simulamos a 260 periodos en el futuro (1 año) y realizamos el mismo procedimiento 1000 veces" ] }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "future <- 260 #52 semanas y 5 dìas a la semana para evaluar a un año\n", - "simulations <- 1000" - ] - }, { "cell_type": "code", "execution_count": 47, @@ -1909,33 +2295,40 @@ }, { "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## TES UVR" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, + "outputs": [], "source": [ - "### Calibración de la curva" + "i_tes_uvr <- tes_uvr_ns[-nrow(tes_uvr_ns),]\n", + "f_tes_uvr <- tes_uvr_ns[-1,]\n", + "delta_tes_uvr_ns <- f_tes_uvr - i_tes_uvr" ] }, { "cell_type": "code", - "execution_count": 67, - "metadata": { - "vscode": { - "languageId": "r" - } - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "0.424283357506555" - ], - "text/latex": [ - "0.424283357506555" - ], - "text/markdown": [ - "0.424283357506555" - ], "text/plain": [ - "[1] 0.4242834" + " beta_0 beta_1 beta_2 lambda \n", + " Min. :-0.03172 Min. :-0.14127 Min. :-0.10238 Min. :3.7 \n", + " 1st Qu.: 0.08206 1st Qu.:-0.05854 1st Qu.:-0.01466 1st Qu.:3.7 \n", + " Median : 0.08882 Median :-0.04336 Median : 0.01870 Median :3.7 \n", + " Mean : 0.09619 Mean :-0.04391 Mean : 0.02938 Mean :3.7 \n", + " 3rd Qu.: 0.10313 3rd Qu.:-0.02884 3rd Qu.: 0.05738 3rd Qu.:3.7 \n", + " Max. : 0.22068 Max. : 0.10092 Max. : 0.45919 Max. :3.7 " ] }, "metadata": {}, @@ -1943,32 +2336,29 @@ } ], "source": [ - "gamma <- log(0.6)/log(0.3)\n", - "gamma" + "summary(tes_uvr_ns) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notese que lambda es una constante, por eso no se incluye en el análisis de PCA**" ] }, { "cell_type": "code", - "execution_count": 68, - "metadata": { - "vscode": { - "languageId": "r" - } - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "0.575716642493445" - ], - "text/latex": [ - "0.575716642493445" - ], - "text/markdown": [ - "0.575716642493445" - ], "text/plain": [ - "[1] 0.5757166" + "Importance of components:\n", + " PC1 PC2 PC3\n", + "Standard deviation 1.6857 0.39083 0.07464\n", + "Proportion of Variance 0.9472 0.05092 0.00186\n", + "Cumulative Proportion 0.9472 0.99814 1.00000" ] }, "metadata": {}, @@ -1976,13 +2366,826 @@ } ], "source": [ - "1- gamma" + "pca_tes_uvr <- prcomp(delta_tes_uvr_ns[,1:3], scale=TRUE)\n", + "summary(pca_tes_uvr)" ] - } - ], - "metadata": { - "interpreter": { - "hash": "aa5dfc9bc22e8b9bd7d7586b99fc6c73114eec00d3f85e09b6fdc2730a892aa2" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dado que el componente principal nos da 94% de la variación. Nos quedamos unicamente con este" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_tes_uvr$residuals\n", + "X-squared = 0.00085204, df = 1, p-value = 0.9767\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_pca_1_tes_uvr <- auto.arima(pca_tes_uvr$x[,1],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_1_tes_uvr$residuals)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "sim_pca_1_tes_uvr <- replicate(expr = simulate(object = arima_pca_1_tes_uvr,nsim = future),n = simulations)\n", + "Bt1 <- pca_tes_uvr$rotation[,1] #Eigen values del primer componente\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "delta<-matrix(,nrow=future, ncol=simulations)\n", + "\n", + "for (i in 1:future){\n", + " for (j in 1:simulations){\n", + " delta[i,j] <- sum(sim_pca_1_tes_uvr[i,j]*Bt1)\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "f_ns_tes_uvr <- list()\n", + "alpha <- tes_uvr_ns[nrow(tes_uvr_ns),] \n", + "alphanuevo <- alpha + 2\n", + "for (i in 1:simulations){\n", + " newalph <- alpha\n", + " for (j in 1:future){\n", + " newalph <- newalph + delta[j,i] \n", + " if (j==260){\n", + " f_ns_tes_uvr <- append(f_ns_tes_uvr, newalph) #Guardamos unicamente los resultados del último periodo pues es la frontera a un año\n", + " }\n", + " }\n", + "}\n", + "mat_f_ns_tes_uvr <- matrix(f_ns_tes_uvr, ncol=4,byrow=T)\n", + "mat_f_ns_tes_uvr <- as.data.frame(mat_f_ns_tes_uvr )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "names(mat_f_ns_tes_uvr) <- c('beta_0','beta_1', 'beta_2', 'lambda')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mat_f_ns_tes_uvr<- data.frame(lapply(mat_f_ns_tes_cop, function(x) as.numeric(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uvr_f_rates <- nelson_sieguel_rate(mat_f_ns_tes_uvr,0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Corporativo a EEUU" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "i_corp_usa <- corp_usa_ns[-nrow(corp_usa_ns),]\n", + "f_corp_usa <- corp_usa_ns[-1,]\n", + "delta_corp_usa_ns <- f_corp_usa - i_corp_usa" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 4
beta_0beta_1beta_2lambda
<dbl><dbl><dbl><dbl>
2009-10-14 0.26974428-0.32308707 0.52626276-0.07359831
2009-10-15-0.12423572 0.12326676-0.47464279 0.07359831
2009-10-16-0.05640593 0.18798050-0.15169724 0.00000000
2009-10-19-0.05557738 0.01640991 0.11722380 0.00000000
2009-10-20-0.01920371 0.01273971-0.13088711 0.00000000
2009-10-21 0.06431601-0.09982943-0.01404811 0.00000000
\n" + ], + "text/latex": [ + "A data.frame: 6 × 4\n", + "\\begin{tabular}{r|llll}\n", + " & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t2009-10-14 & 0.26974428 & -0.32308707 & 0.52626276 & -0.07359831\\\\\n", + "\t2009-10-15 & -0.12423572 & 0.12326676 & -0.47464279 & 0.07359831\\\\\n", + "\t2009-10-16 & -0.05640593 & 0.18798050 & -0.15169724 & 0.00000000\\\\\n", + "\t2009-10-19 & -0.05557738 & 0.01640991 & 0.11722380 & 0.00000000\\\\\n", + "\t2009-10-20 & -0.01920371 & 0.01273971 & -0.13088711 & 0.00000000\\\\\n", + "\t2009-10-21 & 0.06431601 & -0.09982943 & -0.01404811 & 0.00000000\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 4\n", + "\n", + "| | beta_0 <dbl> | beta_1 <dbl> | beta_2 <dbl> | lambda <dbl> |\n", + "|---|---|---|---|---|\n", + "| 2009-10-14 | 0.26974428 | -0.32308707 | 0.52626276 | -0.07359831 |\n", + "| 2009-10-15 | -0.12423572 | 0.12326676 | -0.47464279 | 0.07359831 |\n", + "| 2009-10-16 | -0.05640593 | 0.18798050 | -0.15169724 | 0.00000000 |\n", + "| 2009-10-19 | -0.05557738 | 0.01640991 | 0.11722380 | 0.00000000 |\n", + "| 2009-10-20 | -0.01920371 | 0.01273971 | -0.13088711 | 0.00000000 |\n", + "| 2009-10-21 | 0.06431601 | -0.09982943 | -0.01404811 | 0.00000000 |\n", + "\n" + ], + "text/plain": [ + " beta_0 beta_1 beta_2 lambda \n", + "2009-10-14 0.26974428 -0.32308707 0.52626276 -0.07359831\n", + "2009-10-15 -0.12423572 0.12326676 -0.47464279 0.07359831\n", + "2009-10-16 -0.05640593 0.18798050 -0.15169724 0.00000000\n", + "2009-10-19 -0.05557738 0.01640991 0.11722380 0.00000000\n", + "2009-10-20 -0.01920371 0.01273971 -0.13088711 0.00000000\n", + "2009-10-21 0.06431601 -0.09982943 -0.01404811 0.00000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(delta_corp_usa_ns)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Importance of components:\n", + " PC1 PC2 PC3 PC4\n", + "Standard deviation 1.5543 1.147 0.49601 0.14860\n", + "Proportion of Variance 0.6039 0.329 0.06151 0.00552\n", + "Cumulative Proportion 0.6039 0.933 0.99448 1.00000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca_corp_usa <- prcomp(delta_corp_usa_ns, scale=TRUE)\n", + "summary(pca_corp_usa)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Los primeros 2 componentes capturan el 94 % de la variación por lo que se hace el análisis con ambos." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_corp_usa$residuals\n", + "X-squared = 0.0030951, df = 1, p-value = 0.9556\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_corp_usa$residuals\n", + "X-squared = 0.0030951, df = 1, p-value = 0.9556\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_pca_1_corp_usa <- auto.arima(pca_corp_usa$x[,1],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_1_corp_usa$residuals)\n", + "arima_pca_2_corp_usa <- auto.arima(pca_corp_usa$x[,2],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_1_corp_usa$residuals)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "sim_pca_1_corp_usa <- replicate(expr = simulate(object = arima_pca_1_corp_usa,nsim = future),n = simulations)\n", + "sim_pca_2_corp_usa <- replicate(expr = simulate(object =arima_pca_2_corp_usa,nsim = future),n = simulations)\n", + "Bt1 <- pca_corp_usa$rotation[,1] #Eigen values del primer componente\n", + "Bt2 <- pca_corp_usa$rotation[,2] #Eigen values del segundo componente" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "delta<-matrix(,nrow=future, ncol=simulations)\n", + "\n", + "for (i in 1:future){\n", + " for (j in 1:simulations){\n", + " delta[i,j] <- sum(sim_pca_1_corp_usa[i,j]*Bt1)+sum(sim_pca_2_corp_usa[i,j]*Bt2)\n", + " }\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "f_ns_corp_usa <- list()\n", + "alpha <- corp_usa_ns[nrow(tes_cop_ns),] \n", + "alphanuevo <- alpha + 2\n", + "for (i in 1:simulations){\n", + " newalph <- alpha\n", + " for (j in 1:future){\n", + " newalph <- newalph + delta[j,i] \n", + " if (j==260){\n", + " f_ns_corp_usa <- append(f_ns_corp_usa, newalph) #Guardamos unicamente los resultados del último periodo pues es la frontera a un año\n", + " }\n", + " }\n", + "}\n", + "mat_f_ns_corp_usa <- matrix(f_ns_corp_usa, ncol=4,byrow=T)\n", + "mat_f_ns_corp_usa <- as.data.frame(mat_f_ns_corp_usa )" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "names(mat_f_ns_corp_usa) <- c('beta_0','beta_1', 'beta_2', 'lambda')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "mat_f_ns_corp_usa<- data.frame(lapply(mat_f_ns_corp_usa, function(x) as.numeric(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " beta_0 beta_1 beta_2 lambda \n", + " Min. :-14.6913 Min. :-21.410 Min. :-22.292 Min. :-18.9215 \n", + " 1st Qu.: 0.3466 1st Qu.: -6.372 1st Qu.: -7.254 1st Qu.: -3.8836 \n", + " Median : 4.9406 Median : -1.778 Median : -2.660 Median : 0.7104 \n", + " Mean : 4.9535 Mean : -1.766 Mean : -2.647 Mean : 0.7233 \n", + " 3rd Qu.: 9.5684 3rd Qu.: 2.849 3rd Qu.: 1.968 3rd Qu.: 5.3383 \n", + " Max. : 26.3094 Max. : 19.590 Max. : 18.708 Max. : 22.0792 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " beta_0 beta_1 beta_2 lambda \n", + " Min. : 3.229 Min. :-10.415 Min. :-7.2610 Min. :0.1216 \n", + " 1st Qu.: 4.799 1st Qu.: -6.305 1st Qu.:-4.6519 1st Qu.:0.1939 \n", + " Median : 5.671 Median : -4.996 Median :-3.6463 Median :0.2657 \n", + " Mean : 5.865 Mean : -5.001 Mean :-3.1210 Mean :0.2910 \n", + " 3rd Qu.: 6.760 3rd Qu.: -3.614 3rd Qu.:-0.8426 3rd Qu.:0.4219 \n", + " Max. :10.781 Max. : -1.194 Max. : 0.1801 Max. :1.0000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(mat_f_ns_corp_usa)\n", + "summary(corp_usa_ns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tes_f_rates <- nelson_sieguel_rate(mat_f_ns_corp_usa,0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## COLCAP y SPX" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "i_index <- index[-nrow(index),]\n", + "f_index <- index[-1,]\n", + "delta_index <- f_index - i_index" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_colcap$residuals\n", + "X-squared = 4.1225e-05, df = 1, p-value = 0.9949\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_spx$residuals\n", + "X-squared = 0.75103, df = 1, p-value = 0.3862\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_colcap <- auto.arima(delta_index[,1],stepwise = T,approximation = F)\n", + "Box.test(arima_colcap$residuals)\n", + "arima_spx <- auto.arima(delta_index[,2],stepwise = T,approximation = F)\n", + "Box.test(arima_spx$residuals)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "sim_arima_colcap <- replicate(expr = simulate(object = arima_colcap,nsim = future),n = simulations)\n", + "sim_arima_spx <- replicate(expr = simulate(object =arima_spx,nsim = future),n = simulations)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "f_colcap <- list()\n", + "f_spx <- list()\n", + "alpha_1 <- ipc$ipc[nrow(index),1] \n", + "alpha_2 <- index[nrow(index),2] \n", + "for (i in 1:simulations){\n", + " newalph_1 <- alpha_1\n", + " newalph_2 <- alpha_2\n", + " for (j in 1:future){\n", + " newalph_1 <- newalph_1 + sim_arima_colcap[j,i] \n", + " newalph_2 <- newalph_2 + sim_arima_spx[j,i] \n", + " if (j==260){\n", + " f_colcap <- append(f_colcap, newalph_1) #Guardamos unicamente los resultados del último periodo pues es la frontera a un año\n", + " f_spx <- append(f_spx, newalph_2)\n", + " }\n", + " }\n", + "}\n", + "mat_f_colcap <- matrix(f_colcap, ncol=1,byrow=T)\n", + "mat_f_colcap <- as.data.frame(mat_f_colcap)\n", + "mat_f_spx <- matrix(f_spx, ncol=1,byrow=T)\n", + "mat_f_spx <- as.data.frame(mat_f_spx)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "mat_f_colcap<- data.frame(lapply(mat_f_colcap, function(x) as.numeric(x)))\n", + "mat_f_spx<- data.frame(lapply(mat_f_spx, function(x) as.numeric(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " V1 \n", + " Min. : 723.9 \n", + " 1st Qu.:1436.2 \n", + " Median :1631.8 \n", + " Mean :1622.8 \n", + " 3rd Qu.:1815.7 \n", + " Max. :2533.5 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", + " 894 1347 1495 1494 1653 1942 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " V1 \n", + " Min. :3928 \n", + " 1st Qu.:4641 \n", + " Median :4839 \n", + " Mean :4831 \n", + " 3rd Qu.:5038 \n", + " Max. :5705 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", + " 1023 1435 2089 2284 2820 4797 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(mat_f_colcap)\n", + "summary(index[,1])\n", + "summary(mat_f_spx)\n", + "summary(index[,2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IPC y TRM" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "i_ipc <- ipc$ipc[-nrow(ipc)]\n", + "f_ipc <- ipc$ipc[-1]\n", + "delta_ipc <- f_ipc - i_ipc" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "i_trm <- trm$trm[-nrow(trm)]\n", + "f_trm <- trm$trm[-1]\n", + "delta_trm <- f_trm - i_trm" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_ipc$residuals\n", + "X-squared = 1.4667, df = 1, p-value = 0.2259\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_trm$residuals\n", + "X-squared = 0.00036673, df = 1, p-value = 0.9847\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_ipc <- auto.arima(delta_ipc,stepwise = T,approximation = F)\n", + "Box.test(arima_ipc$residuals)\n", + "arima_trm <- auto.arima(delta_trm,stepwise = T,approximation = F)\n", + "Box.test(arima_trm$residuals)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "sim_arima_ipc <- replicate(expr = simulate(object = arima_ipc,nsim = future),n = simulations)\n", + "sim_arima_trm <- replicate(expr = simulate(object =arima_trm,nsim = future),n = simulations)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "f_ipc <- list()\n", + "f_trm <- list()\n", + "alpha_1 <- ipc$ipc[nrow(ipc)] \n", + "alpha_2 <- trm$trm[nrow(trm)] \n", + "for (i in 1:simulations){\n", + " newalph_1 <- alpha_1\n", + " newalph_2 <- alpha_2\n", + " for (j in 1:future){\n", + " newalph_1 <- newalph_1 + sim_arima_ipc[j,i] \n", + " newalph_2 <- newalph_2 + sim_arima_trm[j,i] \n", + " if (j==260){\n", + " f_ipc <- append(f_ipc, newalph_1) #Guardamos unicamente los resultados del último periodo pues es la frontera a un año\n", + " f_trm <- append(f_trm, newalph_2)\n", + " }\n", + " }\n", + "}\n", + "mat_f_ipc <- matrix(f_ipc, ncol=1,byrow=T)\n", + "mat_f_ipc <- as.data.frame(mat_f_ipc)\n", + "mat_f_trm <- matrix(f_trm, ncol=1,byrow=T)\n", + "mat_f_trm <- as.data.frame(mat_f_trm)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "mat_f_ipc<- data.frame(lapply(mat_f_ipc, function(x) as.numeric(x)))\n", + "mat_f_trm<- data.frame(lapply(mat_f_trm, function(x) as.numeric(x)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " V1 \n", + " Min. :-66.04 \n", + " 1st Qu.:280.57 \n", + " Median :354.08 \n", + " Mean :357.35 \n", + " 3rd Qu.:437.31 \n", + " Max. :738.79 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Date ipc \n", + " Min. :1954-07-31 00:00:00 Min. : 0.02576 \n", + " 1st Qu.:1971-06-30 00:00:00 1st Qu.: 0.12935 \n", + " Median :1988-05-31 00:00:00 Median : 4.16885 \n", + " Mean :1988-05-30 20:48:42 Mean : 28.11323 \n", + " 3rd Qu.:2005-04-30 00:00:00 3rd Qu.: 57.71621 \n", + " Max. :2022-03-31 00:00:00 Max. :116.26000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " V1 \n", + " Min. :3179 \n", + " 1st Qu.:3662 \n", + " Median :3825 \n", + " Mean :3828 \n", + " 3rd Qu.:3974 \n", + " Max. :4551 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " Date trm \n", + " Min. :1991-11-27 00:00:00 Min. : 620.6 \n", + " 1st Qu.:1999-07-01 12:00:00 1st Qu.:1699.7 \n", + " Median :2007-02-03 00:00:00 Median :2103.1 \n", + " Mean :2007-02-03 00:00:00 Mean :2134.1 \n", + " 3rd Qu.:2014-09-07 12:00:00 3rd Qu.:2816.6 \n", + " Max. :2022-04-12 00:00:00 Max. :4153.9 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(mat_f_ipc)\n", + "summary(ipc)\n", + "summary(mat_f_trm)\n", + "summary(trm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calibración de la curva" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "0.424283357506555" + ], + "text/latex": [ + "0.424283357506555" + ], + "text/markdown": [ + "0.424283357506555" + ], + "text/plain": [ + "[1] 0.4242834" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gamma <- log(0.6)/log(0.3)\n", + "gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "0.575716642493445" + ], + "text/latex": [ + "0.575716642493445" + ], + "text/markdown": [ + "0.575716642493445" + ], + "text/plain": [ + "[1] 0.5757166" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "1- gamma" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "55e5e45f2c7483bd6dc7929c8833acb9fd534bb579ffc2e2f3d3d3e99b23c691" }, "kernelspec": { "display_name": "R", @@ -1996,9 +3199,8 @@ "name": "R", "pygments_lexer": "r", "version": "3.6.1" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 4e623f4e609db4065d5e4323ba2dad6a4cf80a46 Mon Sep 17 00:00:00 2001 From: yourusernamedccontreras Date: Fri, 6 May 2022 16:54:54 -0500 Subject: [PATCH 10/10] Final de simulaciones y portafolio --- Taller 2/Scripts/first.ipynb | 1477 ++++++++++++++++++++++++++-------- 1 file changed, 1144 insertions(+), 333 deletions(-) diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb index da4616e..a0dabdc 100644 --- a/Taller 2/Scripts/first.ipynb +++ b/Taller 2/Scripts/first.ipynb @@ -178,7 +178,7 @@ "tags": [] }, "source": [ - "### Simulación de personas" + "## Simulación de personas" ] }, { @@ -996,24 +996,6 @@ "ggplot(c_l_sims,aes(period_f,flow))+ geom_col(fill='chartreuse4') + labs(x=\"años en el futuro\", y='Billones de pesos',title='Flujos anuales de la compañia') +theme_classic()" ] }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "ename": "ERROR", - "evalue": "Error in parse(text = x, srcfile = src): :1:8: unexpected symbol\n1: !conda install\n ^\n", - "output_type": "error", - "traceback": [ - "Error in parse(text = x, srcfile = src): :1:8: unexpected symbol\n1: !conda install\n ^\nTraceback:\n" - ] - } - ], - "source": [ - "!conda install jupyterthemes" - ] - }, { "cell_type": "code", "execution_count": 49, @@ -1138,28 +1120,92 @@ "languageId": "r" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 7
Datetes_cop_6mtes_cop_1ytes_cop_3ytes_cop_5ytes_cop_10ytes_cop_15y
<dttm><dbl><dbl><dbl><dbl><dbl><dbl>
12012-04-275.40425.53286.00566.41396.92767.3809
22012-04-305.37035.51075.99536.42646.94107.3830
32012-05-015.37495.51766.00446.43126.94307.3839
42012-05-025.41765.55136.06136.42816.93087.3765
52012-05-035.43045.55086.04956.40766.88967.3203
62012-05-045.45705.57366.09116.43636.90427.3267
\n" + ], + "text/latex": [ + "A data.frame: 6 × 7\n", + "\\begin{tabular}{r|lllllll}\n", + " & Date & tes\\_cop\\_6m & tes\\_cop\\_1y & tes\\_cop\\_3y & tes\\_cop\\_5y & tes\\_cop\\_10y & tes\\_cop\\_15y\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 2012-04-27 & 5.4042 & 5.5328 & 6.0056 & 6.4139 & 6.9276 & 7.3809\\\\\n", + "\t2 & 2012-04-30 & 5.3703 & 5.5107 & 5.9953 & 6.4264 & 6.9410 & 7.3830\\\\\n", + "\t3 & 2012-05-01 & 5.3749 & 5.5176 & 6.0044 & 6.4312 & 6.9430 & 7.3839\\\\\n", + "\t4 & 2012-05-02 & 5.4176 & 5.5513 & 6.0613 & 6.4281 & 6.9308 & 7.3765\\\\\n", + "\t5 & 2012-05-03 & 5.4304 & 5.5508 & 6.0495 & 6.4076 & 6.8896 & 7.3203\\\\\n", + "\t6 & 2012-05-04 & 5.4570 & 5.5736 & 6.0911 & 6.4363 & 6.9042 & 7.3267\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 7\n", + "\n", + "| | Date <dttm> | tes_cop_6m <dbl> | tes_cop_1y <dbl> | tes_cop_3y <dbl> | tes_cop_5y <dbl> | tes_cop_10y <dbl> | tes_cop_15y <dbl> |\n", + "|---|---|---|---|---|---|---|---|\n", + "| 1 | 2012-04-27 | 5.4042 | 5.5328 | 6.0056 | 6.4139 | 6.9276 | 7.3809 |\n", + "| 2 | 2012-04-30 | 5.3703 | 5.5107 | 5.9953 | 6.4264 | 6.9410 | 7.3830 |\n", + "| 3 | 2012-05-01 | 5.3749 | 5.5176 | 6.0044 | 6.4312 | 6.9430 | 7.3839 |\n", + "| 4 | 2012-05-02 | 5.4176 | 5.5513 | 6.0613 | 6.4281 | 6.9308 | 7.3765 |\n", + "| 5 | 2012-05-03 | 5.4304 | 5.5508 | 6.0495 | 6.4076 | 6.8896 | 7.3203 |\n", + "| 6 | 2012-05-04 | 5.4570 | 5.5736 | 6.0911 | 6.4363 | 6.9042 | 7.3267 |\n", + "\n" + ], + "text/plain": [ + " Date tes_cop_6m tes_cop_1y tes_cop_3y tes_cop_5y tes_cop_10y\n", + "1 2012-04-27 5.4042 5.5328 6.0056 6.4139 6.9276 \n", + "2 2012-04-30 5.3703 5.5107 5.9953 6.4264 6.9410 \n", + "3 2012-05-01 5.3749 5.5176 6.0044 6.4312 6.9430 \n", + "4 2012-05-02 5.4176 5.5513 6.0613 6.4281 6.9308 \n", + "5 2012-05-03 5.4304 5.5508 6.0495 6.4076 6.8896 \n", + "6 2012-05-04 5.4570 5.5736 6.0911 6.4363 6.9042 \n", + " tes_cop_15y\n", + "1 7.3809 \n", + "2 7.3830 \n", + "3 7.3839 \n", + "4 7.3765 \n", + "5 7.3203 \n", + "6 7.3267 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tes_cop <- tes_cop[tes_cop$Date >= \"2012-04-26\",]\n", "tes_cop <- as.data.frame(tes_cop)\n", - "rownames(tes_cop) <- tes_cop$Date\n", - "tes_cop <- tes_cop[,-c(1)]\n", - "tes_cop <- sapply(tes_cop, as.numeric)\n", - "\n" + "# rownames(tes_cop) <- tes_cop$Date\n", + "# tes_cop <- tes_cop[,-c(1)]\n", + "tes_cop[,-c(1)] <- sapply(tes_cop[,-c(1)], as.numeric)\n", + "colnames( tes_cop) <- c('Date','tes_cop_6m','tes_cop_1y','tes_cop_3y','tes_cop_5y','tes_cop_10y','tes_cop_15y')\n", + "head(tes_cop)" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "vscode": { - "languageId": "r" - } - }, + "metadata": {}, "outputs": [], "source": [ - "tes_maturity <- c(0.5,1,3,5,10,15)\n", - "tes_cop_ns <- Nelson.Siegel( rate=tes_cop,\tmaturity=tes_maturity)\n" + "tes_maturity <- c(0.5,1,3,5,10,15)" ] }, { @@ -1183,57 +1229,57 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A data.frame: 6 × 4A data.frame: 6 × 5
beta_0beta_1beta_2lambda
<dbl><dbl><dbl><dbl>
Datebeta_0beta_1beta_2lambda
<dttm><dbl><dbl><dbl><dbl>
2022-04-080.09647801-0.031194060.045114673.7
2022-04-070.09405698-0.029692440.042260963.7
2022-04-060.09469592-0.029902990.042488663.7
2022-04-050.09408631-0.028839650.041866473.7
2022-04-040.09664698-0.033902160.043123843.7
2022-04-010.09685086-0.032173700.044684123.7
12022-04-080.09647801-0.031194060.045114673.7
22022-04-070.09405698-0.029692440.042260963.7
32022-04-060.09469592-0.029902990.042488663.7
42022-04-050.09408631-0.028839650.041866473.7
52022-04-040.09664698-0.033902160.043123843.7
62022-04-010.09685086-0.032173700.044684123.7
\n" ], "text/latex": [ - "A data.frame: 6 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", - " & & & & \\\\\n", + "A data.frame: 6 × 5\n", + "\\begin{tabular}{r|lllll}\n", + " & Date & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", + " & & & & & \\\\\n", "\\hline\n", - "\t2022-04-08 & 0.09647801 & -0.03119406 & 0.04511467 & 3.7\\\\\n", - "\t2022-04-07 & 0.09405698 & -0.02969244 & 0.04226096 & 3.7\\\\\n", - "\t2022-04-06 & 0.09469592 & -0.02990299 & 0.04248866 & 3.7\\\\\n", - "\t2022-04-05 & 0.09408631 & -0.02883965 & 0.04186647 & 3.7\\\\\n", - "\t2022-04-04 & 0.09664698 & -0.03390216 & 0.04312384 & 3.7\\\\\n", - "\t2022-04-01 & 0.09685086 & -0.03217370 & 0.04468412 & 3.7\\\\\n", + "\t1 & 2022-04-08 & 0.09647801 & -0.03119406 & 0.04511467 & 3.7\\\\\n", + "\t2 & 2022-04-07 & 0.09405698 & -0.02969244 & 0.04226096 & 3.7\\\\\n", + "\t3 & 2022-04-06 & 0.09469592 & -0.02990299 & 0.04248866 & 3.7\\\\\n", + "\t4 & 2022-04-05 & 0.09408631 & -0.02883965 & 0.04186647 & 3.7\\\\\n", + "\t5 & 2022-04-04 & 0.09664698 & -0.03390216 & 0.04312384 & 3.7\\\\\n", + "\t6 & 2022-04-01 & 0.09685086 & -0.03217370 & 0.04468412 & 3.7\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.frame: 6 × 4\n", + "A data.frame: 6 × 5\n", "\n", - "| | beta_0 <dbl> | beta_1 <dbl> | beta_2 <dbl> | lambda <dbl> |\n", - "|---|---|---|---|---|\n", - "| 2022-04-08 | 0.09647801 | -0.03119406 | 0.04511467 | 3.7 |\n", - "| 2022-04-07 | 0.09405698 | -0.02969244 | 0.04226096 | 3.7 |\n", - "| 2022-04-06 | 0.09469592 | -0.02990299 | 0.04248866 | 3.7 |\n", - "| 2022-04-05 | 0.09408631 | -0.02883965 | 0.04186647 | 3.7 |\n", - "| 2022-04-04 | 0.09664698 | -0.03390216 | 0.04312384 | 3.7 |\n", - "| 2022-04-01 | 0.09685086 | -0.03217370 | 0.04468412 | 3.7 |\n", + "| | Date <dttm> | beta_0 <dbl> | beta_1 <dbl> | beta_2 <dbl> | lambda <dbl> |\n", + "|---|---|---|---|---|---|\n", + "| 1 | 2022-04-08 | 0.09647801 | -0.03119406 | 0.04511467 | 3.7 |\n", + "| 2 | 2022-04-07 | 0.09405698 | -0.02969244 | 0.04226096 | 3.7 |\n", + "| 3 | 2022-04-06 | 0.09469592 | -0.02990299 | 0.04248866 | 3.7 |\n", + "| 4 | 2022-04-05 | 0.09408631 | -0.02883965 | 0.04186647 | 3.7 |\n", + "| 5 | 2022-04-04 | 0.09664698 | -0.03390216 | 0.04312384 | 3.7 |\n", + "| 6 | 2022-04-01 | 0.09685086 | -0.03217370 | 0.04468412 | 3.7 |\n", "\n" ], "text/plain": [ - " beta_0 beta_1 beta_2 lambda\n", - "2022-04-08 0.09647801 -0.03119406 0.04511467 3.7 \n", - "2022-04-07 0.09405698 -0.02969244 0.04226096 3.7 \n", - "2022-04-06 0.09469592 -0.02990299 0.04248866 3.7 \n", - "2022-04-05 0.09408631 -0.02883965 0.04186647 3.7 \n", - "2022-04-04 0.09664698 -0.03390216 0.04312384 3.7 \n", - "2022-04-01 0.09685086 -0.03217370 0.04468412 3.7 " + " Date beta_0 beta_1 beta_2 lambda\n", + "1 2022-04-08 0.09647801 -0.03119406 0.04511467 3.7 \n", + "2 2022-04-07 0.09405698 -0.02969244 0.04226096 3.7 \n", + "3 2022-04-06 0.09469592 -0.02990299 0.04248866 3.7 \n", + "4 2022-04-05 0.09408631 -0.02883965 0.04186647 3.7 \n", + "5 2022-04-04 0.09664698 -0.03390216 0.04312384 3.7 \n", + "6 2022-04-01 0.09685086 -0.03217370 0.04468412 3.7 " ] }, "metadata": {}, @@ -1243,26 +1289,29 @@ "source": [ "tes_uvr_ns <- read_excel(path, sheet='TES UVR Betas',skip=1, col_names=c('Date','beta_0','beta_1','beta_2','lambda'))\n", "tes_uvr_ns <- as.data.frame(tes_uvr_ns)\n", - "rownames(tes_uvr_ns) <- tes_uvr_ns$Date\n", - "tes_uvr_ns <- tes_uvr_ns[,-c(1)]\n", + "# rownames(tes_uvr_ns) <- tes_uvr_ns$Date\n", + "# tes_uvr_ns <- tes_uvr_ns[,-c(1)]\n", "head(tes_uvr_ns)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, + "outputs": [], "source": [ - "### Corporativo a EEUU" + "nelson_sieguel_rate <- function(insumo,maturity){\n", + " x <- insumo[,1] + insumo[,2]*((1 - exp(-insumo[,4]*maturity))/(insumo[,4]*maturity))+insumo[,3]*(((1- exp(-insumo[,4]*maturity))/(insumo[,4]*maturity))-exp(-insumo[,4]*maturity))\n", + " x <- as.data.frame(x)\n", + " names(x) <- c('rate')\n", + " return(x)\n", + "}" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "vscode": { - "languageId": "r" - } - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -1270,55 +1319,62 @@ "\n", "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A data.frame: 6 × 7
3m6m1y3y5y10y15y
<dbl><dbl><dbl><dbl><dbl><dbl><dbl>
tes_uvr_6mtes_uvr_1ytes_uvr_3ytes_uvr_5ytes_uvr_10ytes_uvr_15yDate
<dbl><dbl><dbl><dbl><dbl><dbl><dttm>
2009-10-131.14041.21811.50462.56613.48114.70765.3309
2009-10-141.13811.22081.51962.60133.52934.78435.4388
2009-10-151.10551.19321.50412.62953.57334.83085.4545
2009-10-161.21141.28971.56192.61393.54474.78205.4008
2009-10-191.17781.26271.54202.59973.52834.75025.3610
2009-10-201.15931.24211.50962.54543.47394.72145.3203
19.5725819.9031939.7731429.7230479.6854249.6728832022-04-08
29.3137549.6325059.5188629.4736369.4396679.4283442022-04-07
39.3748499.6962899.5829109.5376239.5036079.4922692022-04-06
49.3437689.6484949.5259259.4790469.4438399.4321032022-04-05
59.4067229.8011539.7477109.7145459.6896219.6813142022-04-04
69.5523959.9043719.7977239.7527109.7188989.7076272022-04-01
\n" ], "text/latex": [ "A data.frame: 6 × 7\n", "\\begin{tabular}{r|lllllll}\n", - " & 3m & 6m & 1y & 3y & 5y & 10y & 15y\\\\\n", - " & & & & & & & \\\\\n", + " & tes\\_uvr\\_6m & tes\\_uvr\\_1y & tes\\_uvr\\_3y & tes\\_uvr\\_5y & tes\\_uvr\\_10y & tes\\_uvr\\_15y & Date\\\\\n", + " & & & & & & & \\\\\n", "\\hline\n", - "\t2009-10-13 & 1.1404 & 1.2181 & 1.5046 & 2.5661 & 3.4811 & 4.7076 & 5.3309\\\\\n", - "\t2009-10-14 & 1.1381 & 1.2208 & 1.5196 & 2.6013 & 3.5293 & 4.7843 & 5.4388\\\\\n", - "\t2009-10-15 & 1.1055 & 1.1932 & 1.5041 & 2.6295 & 3.5733 & 4.8308 & 5.4545\\\\\n", - "\t2009-10-16 & 1.2114 & 1.2897 & 1.5619 & 2.6139 & 3.5447 & 4.7820 & 5.4008\\\\\n", - "\t2009-10-19 & 1.1778 & 1.2627 & 1.5420 & 2.5997 & 3.5283 & 4.7502 & 5.3610\\\\\n", - "\t2009-10-20 & 1.1593 & 1.2421 & 1.5096 & 2.5454 & 3.4739 & 4.7214 & 5.3203\\\\\n", + "\t1 & 9.572581 & 9.903193 & 9.773142 & 9.723047 & 9.685424 & 9.672883 & 2022-04-08\\\\\n", + "\t2 & 9.313754 & 9.632505 & 9.518862 & 9.473636 & 9.439667 & 9.428344 & 2022-04-07\\\\\n", + "\t3 & 9.374849 & 9.696289 & 9.582910 & 9.537623 & 9.503607 & 9.492269 & 2022-04-06\\\\\n", + "\t4 & 9.343768 & 9.648494 & 9.525925 & 9.479046 & 9.443839 & 9.432103 & 2022-04-05\\\\\n", + "\t5 & 9.406722 & 9.801153 & 9.747710 & 9.714545 & 9.689621 & 9.681314 & 2022-04-04\\\\\n", + "\t6 & 9.552395 & 9.904371 & 9.797723 & 9.752710 & 9.718898 & 9.707627 & 2022-04-01\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 7\n", "\n", - "| | 3m <dbl> | 6m <dbl> | 1y <dbl> | 3y <dbl> | 5y <dbl> | 10y <dbl> | 15y <dbl> |\n", + "| | tes_uvr_6m <dbl> | tes_uvr_1y <dbl> | tes_uvr_3y <dbl> | tes_uvr_5y <dbl> | tes_uvr_10y <dbl> | tes_uvr_15y <dbl> | Date <dttm> |\n", "|---|---|---|---|---|---|---|---|\n", - "| 2009-10-13 | 1.1404 | 1.2181 | 1.5046 | 2.5661 | 3.4811 | 4.7076 | 5.3309 |\n", - "| 2009-10-14 | 1.1381 | 1.2208 | 1.5196 | 2.6013 | 3.5293 | 4.7843 | 5.4388 |\n", - "| 2009-10-15 | 1.1055 | 1.1932 | 1.5041 | 2.6295 | 3.5733 | 4.8308 | 5.4545 |\n", - "| 2009-10-16 | 1.2114 | 1.2897 | 1.5619 | 2.6139 | 3.5447 | 4.7820 | 5.4008 |\n", - "| 2009-10-19 | 1.1778 | 1.2627 | 1.5420 | 2.5997 | 3.5283 | 4.7502 | 5.3610 |\n", - "| 2009-10-20 | 1.1593 | 1.2421 | 1.5096 | 2.5454 | 3.4739 | 4.7214 | 5.3203 |\n", + "| 1 | 9.572581 | 9.903193 | 9.773142 | 9.723047 | 9.685424 | 9.672883 | 2022-04-08 |\n", + "| 2 | 9.313754 | 9.632505 | 9.518862 | 9.473636 | 9.439667 | 9.428344 | 2022-04-07 |\n", + "| 3 | 9.374849 | 9.696289 | 9.582910 | 9.537623 | 9.503607 | 9.492269 | 2022-04-06 |\n", + "| 4 | 9.343768 | 9.648494 | 9.525925 | 9.479046 | 9.443839 | 9.432103 | 2022-04-05 |\n", + "| 5 | 9.406722 | 9.801153 | 9.747710 | 9.714545 | 9.689621 | 9.681314 | 2022-04-04 |\n", + "| 6 | 9.552395 | 9.904371 | 9.797723 | 9.752710 | 9.718898 | 9.707627 | 2022-04-01 |\n", "\n" ], "text/plain": [ - " 3m 6m 1y 3y 5y 10y 15y \n", - "2009-10-13 1.1404 1.2181 1.5046 2.5661 3.4811 4.7076 5.3309\n", - "2009-10-14 1.1381 1.2208 1.5196 2.6013 3.5293 4.7843 5.4388\n", - "2009-10-15 1.1055 1.1932 1.5041 2.6295 3.5733 4.8308 5.4545\n", - "2009-10-16 1.2114 1.2897 1.5619 2.6139 3.5447 4.7820 5.4008\n", - "2009-10-19 1.1778 1.2627 1.5420 2.5997 3.5283 4.7502 5.3610\n", - "2009-10-20 1.1593 1.2421 1.5096 2.5454 3.4739 4.7214 5.3203" + " tes_uvr_6m tes_uvr_1y tes_uvr_3y tes_uvr_5y tes_uvr_10y tes_uvr_15y\n", + "1 9.572581 9.903193 9.773142 9.723047 9.685424 9.672883 \n", + "2 9.313754 9.632505 9.518862 9.473636 9.439667 9.428344 \n", + "3 9.374849 9.696289 9.582910 9.537623 9.503607 9.492269 \n", + "4 9.343768 9.648494 9.525925 9.479046 9.443839 9.432103 \n", + "5 9.406722 9.801153 9.747710 9.714545 9.689621 9.681314 \n", + "6 9.552395 9.904371 9.797723 9.752710 9.718898 9.707627 \n", + " Date \n", + "1 2022-04-08\n", + "2 2022-04-07\n", + "3 2022-04-06\n", + "4 2022-04-05\n", + "5 2022-04-04\n", + "6 2022-04-01" ] }, "metadata": {}, @@ -1326,17 +1382,29 @@ } ], "source": [ - "corp_usa <- read_excel(path, sheet='Corporativo A EEUU',skip=8, col_names=c('Date','3m','6m','1y','3y','5y','10y','15y'))\n", + "tes_uvr <- data.frame(matrix(NA, nrow = nrow(tes_uvr_ns), ncol = 6))\n", + "for (j in 1:6){\n", + " mat <- tes_maturity[j]\n", + " tes_uvr[,j]<- (nelson_sieguel_rate(tes_uvr_ns[,-c(1)],mat))*100\n", + "}\n", + "tes_uvr$Date <- tes_uvr_ns$Date\n", "\n", - "corp_usa <- as.data.frame(corp_usa)\n", - "rownames(corp_usa) <- corp_usa$Date\n", - "corp_usa <- corp_usa[,-c(1)]\n", - "head(corp_usa)" + "colnames( tes_uvr) <- c('tes_uvr_6m','tes_uvr_1y','tes_uvr_3y','tes_uvr_5y','tes_uvr_10y','tes_uvr_15y','Date')\n", + "head(tes_uvr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Corporativo a EEUU" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "metadata": { "vscode": { "languageId": "r" @@ -1347,57 +1415,64 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A data.frame: 6 × 4A data.frame: 6 × 8
beta_0beta_1beta_2lambda
<dbl><dbl><dbl><dbl>
Datecorp_usa_3mcorp_usa_6mcorp_usa_1ycorp_usa_3ycorp_usa_5ycorp_usa_10ycorp_usa_15y
<dttm><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2009-10-136.461855-5.425669-4.1697060.5517879
2009-10-146.731600-5.748756-3.6434430.4781896
2009-10-156.607364-5.625489-4.1180860.5517879
2009-10-166.550958-5.437508-4.2697830.5517879
2009-10-196.495381-5.421099-4.1525600.5517879
2009-10-206.476177-5.408359-4.2834470.5517879
12009-10-131.14041.21811.50462.56613.48114.70765.3309
22009-10-141.13811.22081.51962.60133.52934.78435.4388
32009-10-151.10551.19321.50412.62953.57334.83085.4545
42009-10-161.21141.28971.56192.61393.54474.78205.4008
52009-10-191.17781.26271.54202.59973.52834.75025.3610
62009-10-201.15931.24211.50962.54543.47394.72145.3203
\n" ], "text/latex": [ - "A data.frame: 6 × 4\n", - "\\begin{tabular}{r|llll}\n", - " & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", - " & & & & \\\\\n", + "A data.frame: 6 × 8\n", + "\\begin{tabular}{r|llllllll}\n", + " & Date & corp\\_usa\\_3m & corp\\_usa\\_6m & corp\\_usa\\_1y & corp\\_usa\\_3y & corp\\_usa\\_5y & corp\\_usa\\_10y & corp\\_usa\\_15y\\\\\n", + " & & & & & & & & \\\\\n", "\\hline\n", - "\t2009-10-13 & 6.461855 & -5.425669 & -4.169706 & 0.5517879\\\\\n", - "\t2009-10-14 & 6.731600 & -5.748756 & -3.643443 & 0.4781896\\\\\n", - "\t2009-10-15 & 6.607364 & -5.625489 & -4.118086 & 0.5517879\\\\\n", - "\t2009-10-16 & 6.550958 & -5.437508 & -4.269783 & 0.5517879\\\\\n", - "\t2009-10-19 & 6.495381 & -5.421099 & -4.152560 & 0.5517879\\\\\n", - "\t2009-10-20 & 6.476177 & -5.408359 & -4.283447 & 0.5517879\\\\\n", + "\t1 & 2009-10-13 & 1.1404 & 1.2181 & 1.5046 & 2.5661 & 3.4811 & 4.7076 & 5.3309\\\\\n", + "\t2 & 2009-10-14 & 1.1381 & 1.2208 & 1.5196 & 2.6013 & 3.5293 & 4.7843 & 5.4388\\\\\n", + "\t3 & 2009-10-15 & 1.1055 & 1.1932 & 1.5041 & 2.6295 & 3.5733 & 4.8308 & 5.4545\\\\\n", + "\t4 & 2009-10-16 & 1.2114 & 1.2897 & 1.5619 & 2.6139 & 3.5447 & 4.7820 & 5.4008\\\\\n", + "\t5 & 2009-10-19 & 1.1778 & 1.2627 & 1.5420 & 2.5997 & 3.5283 & 4.7502 & 5.3610\\\\\n", + "\t6 & 2009-10-20 & 1.1593 & 1.2421 & 1.5096 & 2.5454 & 3.4739 & 4.7214 & 5.3203\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.frame: 6 × 4\n", + "A data.frame: 6 × 8\n", "\n", - "| | beta_0 <dbl> | beta_1 <dbl> | beta_2 <dbl> | lambda <dbl> |\n", - "|---|---|---|---|---|\n", - "| 2009-10-13 | 6.461855 | -5.425669 | -4.169706 | 0.5517879 |\n", - "| 2009-10-14 | 6.731600 | -5.748756 | -3.643443 | 0.4781896 |\n", - "| 2009-10-15 | 6.607364 | -5.625489 | -4.118086 | 0.5517879 |\n", - "| 2009-10-16 | 6.550958 | -5.437508 | -4.269783 | 0.5517879 |\n", - "| 2009-10-19 | 6.495381 | -5.421099 | -4.152560 | 0.5517879 |\n", - "| 2009-10-20 | 6.476177 | -5.408359 | -4.283447 | 0.5517879 |\n", + "| | Date <dttm> | corp_usa_3m <dbl> | corp_usa_6m <dbl> | corp_usa_1y <dbl> | corp_usa_3y <dbl> | corp_usa_5y <dbl> | corp_usa_10y <dbl> | corp_usa_15y <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 2009-10-13 | 1.1404 | 1.2181 | 1.5046 | 2.5661 | 3.4811 | 4.7076 | 5.3309 |\n", + "| 2 | 2009-10-14 | 1.1381 | 1.2208 | 1.5196 | 2.6013 | 3.5293 | 4.7843 | 5.4388 |\n", + "| 3 | 2009-10-15 | 1.1055 | 1.1932 | 1.5041 | 2.6295 | 3.5733 | 4.8308 | 5.4545 |\n", + "| 4 | 2009-10-16 | 1.2114 | 1.2897 | 1.5619 | 2.6139 | 3.5447 | 4.7820 | 5.4008 |\n", + "| 5 | 2009-10-19 | 1.1778 | 1.2627 | 1.5420 | 2.5997 | 3.5283 | 4.7502 | 5.3610 |\n", + "| 6 | 2009-10-20 | 1.1593 | 1.2421 | 1.5096 | 2.5454 | 3.4739 | 4.7214 | 5.3203 |\n", "\n" ], "text/plain": [ - " beta_0 beta_1 beta_2 lambda \n", - "2009-10-13 6.461855 -5.425669 -4.169706 0.5517879\n", - "2009-10-14 6.731600 -5.748756 -3.643443 0.4781896\n", - "2009-10-15 6.607364 -5.625489 -4.118086 0.5517879\n", - "2009-10-16 6.550958 -5.437508 -4.269783 0.5517879\n", - "2009-10-19 6.495381 -5.421099 -4.152560 0.5517879\n", - "2009-10-20 6.476177 -5.408359 -4.283447 0.5517879" + " Date corp_usa_3m corp_usa_6m corp_usa_1y corp_usa_3y corp_usa_5y\n", + "1 2009-10-13 1.1404 1.2181 1.5046 2.5661 3.4811 \n", + "2 2009-10-14 1.1381 1.2208 1.5196 2.6013 3.5293 \n", + "3 2009-10-15 1.1055 1.1932 1.5041 2.6295 3.5733 \n", + "4 2009-10-16 1.2114 1.2897 1.5619 2.6139 3.5447 \n", + "5 2009-10-19 1.1778 1.2627 1.5420 2.5997 3.5283 \n", + "6 2009-10-20 1.1593 1.2421 1.5096 2.5454 3.4739 \n", + " corp_usa_10y corp_usa_15y\n", + "1 4.7076 5.3309 \n", + "2 4.7843 5.4388 \n", + "3 4.8308 5.4545 \n", + "4 4.7820 5.4008 \n", + "5 4.7502 5.3610 \n", + "6 4.7214 5.3203 " ] }, "metadata": {}, @@ -1405,21 +1480,26 @@ } ], "source": [ - "corp_usa_maturity <- c(3/12,0.5,1,3,5,10,15)\n", - "corp_usa_ns <- Nelson.Siegel( rate=corp_usa, maturity=corp_usa_maturity)\n", - "head(corp_usa_ns)" + "corp_usa <- read_excel(path, sheet='Corporativo A EEUU',skip=8, col_names=c('Date','corp_usa_3m','corp_usa_6m','corp_usa_1y','corp_usa_3y','corp_usa_5y','corp_usa_10y','corp_usa_15y'))\n", + "\n", + "corp_usa <- as.data.frame(corp_usa)\n", + "# rownames(corp_usa) <- corp_usa$Date\n", + "# corp_usa <- corp_usa[,-c(1)]\n", + "head(corp_usa)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### ETF's de seguimiento de la economía colombiana (COLCAP) y estadounidense (SPX)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "metadata": { "vscode": { "languageId": "r" @@ -1430,57 +1510,57 @@ "data": { "text/html": [ "\n", - "\n", + "\n", "\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A data.frame: 6 × 2A data.frame: 6 × 3
colcapspx
<dbl><dbl>
Datecolcapspx
<dttm><dbl><dbl>
2009-10-131303.031073.19
2009-10-141294.391092.02
2009-10-151294.111096.56
2009-10-161301.691087.68
2009-10-191330.801097.91
2009-10-201315.971091.06
12009-10-131303.031073.19
22009-10-141294.391092.02
32009-10-151294.111096.56
42009-10-161301.691087.68
52009-10-191330.801097.91
62009-10-201315.971091.06
\n" ], "text/latex": [ - "A data.frame: 6 × 2\n", - "\\begin{tabular}{r|ll}\n", - " & colcap & spx\\\\\n", - " & & \\\\\n", + "A data.frame: 6 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & Date & colcap & spx\\\\\n", + " & & & \\\\\n", "\\hline\n", - "\t2009-10-13 & 1303.03 & 1073.19\\\\\n", - "\t2009-10-14 & 1294.39 & 1092.02\\\\\n", - "\t2009-10-15 & 1294.11 & 1096.56\\\\\n", - "\t2009-10-16 & 1301.69 & 1087.68\\\\\n", - "\t2009-10-19 & 1330.80 & 1097.91\\\\\n", - "\t2009-10-20 & 1315.97 & 1091.06\\\\\n", + "\t1 & 2009-10-13 & 1303.03 & 1073.19\\\\\n", + "\t2 & 2009-10-14 & 1294.39 & 1092.02\\\\\n", + "\t3 & 2009-10-15 & 1294.11 & 1096.56\\\\\n", + "\t4 & 2009-10-16 & 1301.69 & 1087.68\\\\\n", + "\t5 & 2009-10-19 & 1330.80 & 1097.91\\\\\n", + "\t6 & 2009-10-20 & 1315.97 & 1091.06\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", - "A data.frame: 6 × 2\n", + "A data.frame: 6 × 3\n", "\n", - "| | colcap <dbl> | spx <dbl> |\n", - "|---|---|---|\n", - "| 2009-10-13 | 1303.03 | 1073.19 |\n", - "| 2009-10-14 | 1294.39 | 1092.02 |\n", - "| 2009-10-15 | 1294.11 | 1096.56 |\n", - "| 2009-10-16 | 1301.69 | 1087.68 |\n", - "| 2009-10-19 | 1330.80 | 1097.91 |\n", - "| 2009-10-20 | 1315.97 | 1091.06 |\n", + "| | Date <dttm> | colcap <dbl> | spx <dbl> |\n", + "|---|---|---|---|\n", + "| 1 | 2009-10-13 | 1303.03 | 1073.19 |\n", + "| 2 | 2009-10-14 | 1294.39 | 1092.02 |\n", + "| 3 | 2009-10-15 | 1294.11 | 1096.56 |\n", + "| 4 | 2009-10-16 | 1301.69 | 1087.68 |\n", + "| 5 | 2009-10-19 | 1330.80 | 1097.91 |\n", + "| 6 | 2009-10-20 | 1315.97 | 1091.06 |\n", "\n" ], "text/plain": [ - " colcap spx \n", - "2009-10-13 1303.03 1073.19\n", - "2009-10-14 1294.39 1092.02\n", - "2009-10-15 1294.11 1096.56\n", - "2009-10-16 1301.69 1087.68\n", - "2009-10-19 1330.80 1097.91\n", - "2009-10-20 1315.97 1091.06" + " Date colcap spx \n", + "1 2009-10-13 1303.03 1073.19\n", + "2 2009-10-14 1294.39 1092.02\n", + "3 2009-10-15 1294.11 1096.56\n", + "4 2009-10-16 1301.69 1087.68\n", + "5 2009-10-19 1330.80 1097.91\n", + "6 2009-10-20 1315.97 1091.06" ] }, "metadata": {}, @@ -1490,21 +1570,21 @@ "source": [ "index <- read_excel(path, sheet='Indices Accionarios',skip=7, col_names=c('Date','colcap','spx'))\n", "index <- as.data.frame(index)\n", - "rownames(index) <- index$Date\n", - "index <- index[,-c(1)]\n", "head(index)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Indice de Precios al consumidor" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "metadata": { "vscode": { "languageId": "r" @@ -1581,14 +1661,16 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Tasa de cambio" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 12, "metadata": { "vscode": { "languageId": "r" @@ -1664,7 +1746,122 @@ }, { "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Merge bases" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "joint <- merge(tes_cop, tes_uvr, by='Date')\n", + "joint <- merge(joint, corp_usa, by='Date')\n", + "joint <- merge(joint, index, by='Date')\n", + "joint <- merge(joint, ipc, by='Date')\n", + "joint <- merge(joint, trm, by='Date')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 24
Datetes_cop_6mtes_cop_1ytes_cop_3ytes_cop_5ytes_cop_10ytes_cop_15ytes_uvr_6mtes_uvr_1ytes_uvr_3y...corp_usa_6mcorp_usa_1ycorp_usa_3ycorp_usa_5ycorp_usa_10ycorp_usa_15ycolcapspxipctrm
<dttm><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>...<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
12012-04-305.37035.51075.99536.42646.94107.38306.8719627.4761248.005419...0.61070.71311.17091.75253.04894.08061785.661397.9177.423081761.20
22012-05-315.38935.52856.09996.54367.13317.37946.9703327.3913267.599899...0.68060.78501.22921.74132.89873.86121726.551310.3377.655381827.83
32012-07-315.27975.31945.55895.87386.56016.94856.4241247.2066128.124908...0.49380.63551.15021.64312.64163.64621673.871379.3277.702891789.02
42012-08-314.81664.90885.29195.75546.57726.90736.2357627.1672928.231727...0.43580.59181.09621.62412.68553.69921668.501406.5877.734761830.50
52012-10-314.83474.91135.18195.46566.10466.39205.7651356.4440347.236066...0.46130.56240.98681.47082.57393.65321809.931412.1678.084701829.89
62012-11-304.87634.95505.25025.42185.92196.28595.7047886.3594177.151934...0.48680.59831.01441.49032.64143.73781759.531416.1877.977951817.93
\n" + ], + "text/latex": [ + "A data.frame: 6 × 24\n", + "\\begin{tabular}{r|lllllllllllllllllllll}\n", + " & Date & tes\\_cop\\_6m & tes\\_cop\\_1y & tes\\_cop\\_3y & tes\\_cop\\_5y & tes\\_cop\\_10y & tes\\_cop\\_15y & tes\\_uvr\\_6m & tes\\_uvr\\_1y & tes\\_uvr\\_3y & ... & corp\\_usa\\_6m & corp\\_usa\\_1y & corp\\_usa\\_3y & corp\\_usa\\_5y & corp\\_usa\\_10y & corp\\_usa\\_15y & colcap & spx & ipc & trm\\\\\n", + " & & & & & & & & & & & ... & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t1 & 2012-04-30 & 5.3703 & 5.5107 & 5.9953 & 6.4264 & 6.9410 & 7.3830 & 6.871962 & 7.476124 & 8.005419 & ... & 0.6107 & 0.7131 & 1.1709 & 1.7525 & 3.0489 & 4.0806 & 1785.66 & 1397.91 & 77.42308 & 1761.20\\\\\n", + "\t2 & 2012-05-31 & 5.3893 & 5.5285 & 6.0999 & 6.5436 & 7.1331 & 7.3794 & 6.970332 & 7.391326 & 7.599899 & ... & 0.6806 & 0.7850 & 1.2292 & 1.7413 & 2.8987 & 3.8612 & 1726.55 & 1310.33 & 77.65538 & 1827.83\\\\\n", + "\t3 & 2012-07-31 & 5.2797 & 5.3194 & 5.5589 & 5.8738 & 6.5601 & 6.9485 & 6.424124 & 7.206612 & 8.124908 & ... & 0.4938 & 0.6355 & 1.1502 & 1.6431 & 2.6416 & 3.6462 & 1673.87 & 1379.32 & 77.70289 & 1789.02\\\\\n", + "\t4 & 2012-08-31 & 4.8166 & 4.9088 & 5.2919 & 5.7554 & 6.5772 & 6.9073 & 6.235762 & 7.167292 & 8.231727 & ... & 0.4358 & 0.5918 & 1.0962 & 1.6241 & 2.6855 & 3.6992 & 1668.50 & 1406.58 & 77.73476 & 1830.50\\\\\n", + "\t5 & 2012-10-31 & 4.8347 & 4.9113 & 5.1819 & 5.4656 & 6.1046 & 6.3920 & 5.765135 & 6.444034 & 7.236066 & ... & 0.4613 & 0.5624 & 0.9868 & 1.4708 & 2.5739 & 3.6532 & 1809.93 & 1412.16 & 78.08470 & 1829.89\\\\\n", + "\t6 & 2012-11-30 & 4.8763 & 4.9550 & 5.2502 & 5.4218 & 5.9219 & 6.2859 & 5.704788 & 6.359417 & 7.151934 & ... & 0.4868 & 0.5983 & 1.0144 & 1.4903 & 2.6414 & 3.7378 & 1759.53 & 1416.18 & 77.97795 & 1817.93\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 6 × 24\n", + "\n", + "| | Date <dttm> | tes_cop_6m <dbl> | tes_cop_1y <dbl> | tes_cop_3y <dbl> | tes_cop_5y <dbl> | tes_cop_10y <dbl> | tes_cop_15y <dbl> | tes_uvr_6m <dbl> | tes_uvr_1y <dbl> | tes_uvr_3y <dbl> | ... ... | corp_usa_6m <dbl> | corp_usa_1y <dbl> | corp_usa_3y <dbl> | corp_usa_5y <dbl> | corp_usa_10y <dbl> | corp_usa_15y <dbl> | colcap <dbl> | spx <dbl> | ipc <dbl> | trm <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| 1 | 2012-04-30 | 5.3703 | 5.5107 | 5.9953 | 6.4264 | 6.9410 | 7.3830 | 6.871962 | 7.476124 | 8.005419 | ... | 0.6107 | 0.7131 | 1.1709 | 1.7525 | 3.0489 | 4.0806 | 1785.66 | 1397.91 | 77.42308 | 1761.20 |\n", + "| 2 | 2012-05-31 | 5.3893 | 5.5285 | 6.0999 | 6.5436 | 7.1331 | 7.3794 | 6.970332 | 7.391326 | 7.599899 | ... | 0.6806 | 0.7850 | 1.2292 | 1.7413 | 2.8987 | 3.8612 | 1726.55 | 1310.33 | 77.65538 | 1827.83 |\n", + "| 3 | 2012-07-31 | 5.2797 | 5.3194 | 5.5589 | 5.8738 | 6.5601 | 6.9485 | 6.424124 | 7.206612 | 8.124908 | ... | 0.4938 | 0.6355 | 1.1502 | 1.6431 | 2.6416 | 3.6462 | 1673.87 | 1379.32 | 77.70289 | 1789.02 |\n", + "| 4 | 2012-08-31 | 4.8166 | 4.9088 | 5.2919 | 5.7554 | 6.5772 | 6.9073 | 6.235762 | 7.167292 | 8.231727 | ... | 0.4358 | 0.5918 | 1.0962 | 1.6241 | 2.6855 | 3.6992 | 1668.50 | 1406.58 | 77.73476 | 1830.50 |\n", + "| 5 | 2012-10-31 | 4.8347 | 4.9113 | 5.1819 | 5.4656 | 6.1046 | 6.3920 | 5.765135 | 6.444034 | 7.236066 | ... | 0.4613 | 0.5624 | 0.9868 | 1.4708 | 2.5739 | 3.6532 | 1809.93 | 1412.16 | 78.08470 | 1829.89 |\n", + "| 6 | 2012-11-30 | 4.8763 | 4.9550 | 5.2502 | 5.4218 | 5.9219 | 6.2859 | 5.704788 | 6.359417 | 7.151934 | ... | 0.4868 | 0.5983 | 1.0144 | 1.4903 | 2.6414 | 3.7378 | 1759.53 | 1416.18 | 77.97795 | 1817.93 |\n", + "\n" + ], + "text/plain": [ + " Date tes_cop_6m tes_cop_1y tes_cop_3y tes_cop_5y tes_cop_10y\n", + "1 2012-04-30 5.3703 5.5107 5.9953 6.4264 6.9410 \n", + "2 2012-05-31 5.3893 5.5285 6.0999 6.5436 7.1331 \n", + "3 2012-07-31 5.2797 5.3194 5.5589 5.8738 6.5601 \n", + "4 2012-08-31 4.8166 4.9088 5.2919 5.7554 6.5772 \n", + "5 2012-10-31 4.8347 4.9113 5.1819 5.4656 6.1046 \n", + "6 2012-11-30 4.8763 4.9550 5.2502 5.4218 5.9219 \n", + " tes_cop_15y tes_uvr_6m tes_uvr_1y tes_uvr_3y ... corp_usa_6m corp_usa_1y\n", + "1 7.3830 6.871962 7.476124 8.005419 ... 0.6107 0.7131 \n", + "2 7.3794 6.970332 7.391326 7.599899 ... 0.6806 0.7850 \n", + "3 6.9485 6.424124 7.206612 8.124908 ... 0.4938 0.6355 \n", + "4 6.9073 6.235762 7.167292 8.231727 ... 0.4358 0.5918 \n", + "5 6.3920 5.765135 6.444034 7.236066 ... 0.4613 0.5624 \n", + "6 6.2859 5.704788 6.359417 7.151934 ... 0.4868 0.5983 \n", + " corp_usa_3y corp_usa_5y corp_usa_10y corp_usa_15y colcap spx ipc \n", + "1 1.1709 1.7525 3.0489 4.0806 1785.66 1397.91 77.42308\n", + "2 1.2292 1.7413 2.8987 3.8612 1726.55 1310.33 77.65538\n", + "3 1.1502 1.6431 2.6416 3.6462 1673.87 1379.32 77.70289\n", + "4 1.0962 1.6241 2.6855 3.6992 1668.50 1406.58 77.73476\n", + "5 0.9868 1.4708 2.5739 3.6532 1809.93 1412.16 78.08470\n", + "6 1.0144 1.4903 2.6414 3.7378 1759.53 1416.18 77.97795\n", + " trm \n", + "1 1761.20\n", + "2 1827.83\n", + "3 1789.02\n", + "4 1830.50\n", + "5 1829.89\n", + "6 1817.93" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "head(joint)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, "source": [ "# Simulaciones de proyección\n", "En esta sección se estiman las 1000 simulaciones para cada uno de los productos financieros. En el caso de los bonos TES cop, TES UVR y corp EEUU se realiza el análisis de componentes principales." @@ -1673,7 +1870,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -1782,7 +1978,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 18, "metadata": { "vscode": { "languageId": "r" @@ -1794,20 +1990,9 @@ "simulations <- 1000" ] }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## TES Pesos\n", - "Lo primero es que obtenemos el cambio en los betas y el lambda de NSiegel:" - ] - }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "vscode": { "languageId": "r" @@ -1822,7 +2007,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "vscode": { "languageId": "r" @@ -1901,7 +2086,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "vscode": { "languageId": "r" @@ -1929,7 +2114,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "vscode": { "languageId": "r" @@ -2008,7 +2193,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 27, "metadata": { "vscode": { "languageId": "r" @@ -2022,7 +2207,7 @@ "\tBox-Pierce test\n", "\n", "data: arima_pca_1_tes_cop$residuals\n", - "X-squared = 0.018308, df = 1, p-value = 0.8924\n" + "X-squared = 0.018292, df = 1, p-value = 0.8924\n" ] }, "metadata": {}, @@ -2035,7 +2220,7 @@ "\tBox-Pierce test\n", "\n", "data: arima_pca_1_tes_cop$residuals\n", - "X-squared = 0.018308, df = 1, p-value = 0.8924\n" + "X-squared = 0.018292, df = 1, p-value = 0.8924\n" ] }, "metadata": {}, @@ -2058,7 +2243,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 28, "metadata": { "vscode": { "languageId": "r" @@ -2081,7 +2266,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 29, "metadata": { "vscode": { "languageId": "r" @@ -2094,46 +2279,46 @@ "\n", "\n", "\n", - "\t\n", - "\t\n", - "\t\n", + "\t\n", + "\t\n", + "\t\n", "\n", "
A matrix: 3 × 1000 of type dbl
0.8667266-0.6162629-0.7101092-0.02593674-1.4947931 0.1331738 0.2385630-1.3218142-0.2986355-1.3195469...-0.05672787-0.51160508-0.9750071-0.81583947 0.2662714-1.3493055-0.8440075-0.63457252-1.41531198 0.1629867
-0.8934856 0.6276854 0.8532216 0.14676585 0.5528627-0.2451592-0.4553099-0.2240811 0.5991739-0.0370114...-0.90343711-0.03551573 0.3742178 0.23826482-1.1378925 0.5160537 1.1304199-0.14146838 0.28514031-0.2556540
0.1433665-0.8739295 0.3607352-0.56205363 1.7892906 0.4841982 0.1907714 1.0055555-0.6151569-0.1830808... 0.07051410 0.04185852 0.4738914 0.04853035 1.2690662-1.0985671-0.2457784 0.05247762-0.09210574-0.4420530
-0.1856780-0.1778925-1.02601113-1.92734556-0.3000903 1.315765-0.91823480 0.4103676 0.2669049 0.6059881...-0.4663345-0.0313809 0.014388927 1.03903354-1.32588407-1.3320434 0.4669779-0.6306242-1.4325079-1.60840683
0.5445780-0.9806836-0.07267132 1.58757888 1.0100853 1.023286-0.02164902-0.5937949 0.7180511 0.2410138...-0.1487819-0.4129208-0.817239892-1.22346427 0.37972805 1.1926767-0.4528030 1.0400085 0.7165630-0.11204069
-0.5560755 0.3176130 0.49447835-0.06387172-1.0962893-1.639022-0.17529489-0.4070562-1.3540915-0.1571198...-0.6434944 0.6516976 0.005204321-0.06921579 0.05620399 0.6300882-0.6084632-1.1760778 0.1119728-0.05696253
\n" ], "text/latex": [ "A matrix: 3 × 1000 of type dbl\n", "\\begin{tabular}{lllllllllllllllllllll}\n", - "\t 0.8667266 & -0.6162629 & -0.7101092 & -0.02593674 & -1.4947931 & 0.1331738 & 0.2385630 & -1.3218142 & -0.2986355 & -1.3195469 & ... & -0.05672787 & -0.51160508 & -0.9750071 & -0.81583947 & 0.2662714 & -1.3493055 & -0.8440075 & -0.63457252 & -1.41531198 & 0.1629867\\\\\n", - "\t -0.8934856 & 0.6276854 & 0.8532216 & 0.14676585 & 0.5528627 & -0.2451592 & -0.4553099 & -0.2240811 & 0.5991739 & -0.0370114 & ... & -0.90343711 & -0.03551573 & 0.3742178 & 0.23826482 & -1.1378925 & 0.5160537 & 1.1304199 & -0.14146838 & 0.28514031 & -0.2556540\\\\\n", - "\t 0.1433665 & -0.8739295 & 0.3607352 & -0.56205363 & 1.7892906 & 0.4841982 & 0.1907714 & 1.0055555 & -0.6151569 & -0.1830808 & ... & 0.07051410 & 0.04185852 & 0.4738914 & 0.04853035 & 1.2690662 & -1.0985671 & -0.2457784 & 0.05247762 & -0.09210574 & -0.4420530\\\\\n", + "\t -0.1856780 & -0.1778925 & -1.02601113 & -1.92734556 & -0.3000903 & 1.315765 & -0.91823480 & 0.4103676 & 0.2669049 & 0.6059881 & ... & -0.4663345 & -0.0313809 & 0.014388927 & 1.03903354 & -1.32588407 & -1.3320434 & 0.4669779 & -0.6306242 & -1.4325079 & -1.60840683\\\\\n", + "\t 0.5445780 & -0.9806836 & -0.07267132 & 1.58757888 & 1.0100853 & 1.023286 & -0.02164902 & -0.5937949 & 0.7180511 & 0.2410138 & ... & -0.1487819 & -0.4129208 & -0.817239892 & -1.22346427 & 0.37972805 & 1.1926767 & -0.4528030 & 1.0400085 & 0.7165630 & -0.11204069\\\\\n", + "\t -0.5560755 & 0.3176130 & 0.49447835 & -0.06387172 & -1.0962893 & -1.639022 & -0.17529489 & -0.4070562 & -1.3540915 & -0.1571198 & ... & -0.6434944 & 0.6516976 & 0.005204321 & -0.06921579 & 0.05620399 & 0.6300882 & -0.6084632 & -1.1760778 & 0.1119728 & -0.05696253\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 1000 of type dbl\n", "\n", - "| 0.8667266 | -0.6162629 | -0.7101092 | -0.02593674 | -1.4947931 | 0.1331738 | 0.2385630 | -1.3218142 | -0.2986355 | -1.3195469 | ... | -0.05672787 | -0.51160508 | -0.9750071 | -0.81583947 | 0.2662714 | -1.3493055 | -0.8440075 | -0.63457252 | -1.41531198 | 0.1629867 |\n", - "| -0.8934856 | 0.6276854 | 0.8532216 | 0.14676585 | 0.5528627 | -0.2451592 | -0.4553099 | -0.2240811 | 0.5991739 | -0.0370114 | ... | -0.90343711 | -0.03551573 | 0.3742178 | 0.23826482 | -1.1378925 | 0.5160537 | 1.1304199 | -0.14146838 | 0.28514031 | -0.2556540 |\n", - "| 0.1433665 | -0.8739295 | 0.3607352 | -0.56205363 | 1.7892906 | 0.4841982 | 0.1907714 | 1.0055555 | -0.6151569 | -0.1830808 | ... | 0.07051410 | 0.04185852 | 0.4738914 | 0.04853035 | 1.2690662 | -1.0985671 | -0.2457784 | 0.05247762 | -0.09210574 | -0.4420530 |\n", + "| -0.1856780 | -0.1778925 | -1.02601113 | -1.92734556 | -0.3000903 | 1.315765 | -0.91823480 | 0.4103676 | 0.2669049 | 0.6059881 | ... | -0.4663345 | -0.0313809 | 0.014388927 | 1.03903354 | -1.32588407 | -1.3320434 | 0.4669779 | -0.6306242 | -1.4325079 | -1.60840683 |\n", + "| 0.5445780 | -0.9806836 | -0.07267132 | 1.58757888 | 1.0100853 | 1.023286 | -0.02164902 | -0.5937949 | 0.7180511 | 0.2410138 | ... | -0.1487819 | -0.4129208 | -0.817239892 | -1.22346427 | 0.37972805 | 1.1926767 | -0.4528030 | 1.0400085 | 0.7165630 | -0.11204069 |\n", + "| -0.5560755 | 0.3176130 | 0.49447835 | -0.06387172 | -1.0962893 | -1.639022 | -0.17529489 | -0.4070562 | -1.3540915 | -0.1571198 | ... | -0.6434944 | 0.6516976 | 0.005204321 | -0.06921579 | 0.05620399 | 0.6300882 | -0.6084632 | -1.1760778 | 0.1119728 | -0.05696253 |\n", "\n" ], "text/plain": [ - " [,1] [,2] [,3] [,4] [,5] [,6] \n", - "[1,] 0.8667266 -0.6162629 -0.7101092 -0.02593674 -1.4947931 0.1331738\n", - "[2,] -0.8934856 0.6276854 0.8532216 0.14676585 0.5528627 -0.2451592\n", - "[3,] 0.1433665 -0.8739295 0.3607352 -0.56205363 1.7892906 0.4841982\n", - " [,7] [,8] [,9] [,10] [,11] [,12] [,13] \n", - "[1,] 0.2385630 -1.3218142 -0.2986355 -1.3195469 ... -0.05672787 -0.51160508\n", - "[2,] -0.4553099 -0.2240811 0.5991739 -0.0370114 ... -0.90343711 -0.03551573\n", - "[3,] 0.1907714 1.0055555 -0.6151569 -0.1830808 ... 0.07051410 0.04185852\n", - " [,14] [,15] [,16] [,17] [,18] [,19] \n", - "[1,] -0.9750071 -0.81583947 0.2662714 -1.3493055 -0.8440075 -0.63457252\n", - "[2,] 0.3742178 0.23826482 -1.1378925 0.5160537 1.1304199 -0.14146838\n", - "[3,] 0.4738914 0.04853035 1.2690662 -1.0985671 -0.2457784 0.05247762\n", - " [,20] [,21] \n", - "[1,] -1.41531198 0.1629867\n", - "[2,] 0.28514031 -0.2556540\n", - "[3,] -0.09210574 -0.4420530" + " [,1] [,2] [,3] [,4] [,5] [,6] \n", + "[1,] -0.1856780 -0.1778925 -1.02601113 -1.92734556 -0.3000903 1.315765\n", + "[2,] 0.5445780 -0.9806836 -0.07267132 1.58757888 1.0100853 1.023286\n", + "[3,] -0.5560755 0.3176130 0.49447835 -0.06387172 -1.0962893 -1.639022\n", + " [,7] [,8] [,9] [,10] [,11] [,12] [,13] \n", + "[1,] -0.91823480 0.4103676 0.2669049 0.6059881 ... -0.4663345 -0.0313809\n", + "[2,] -0.02164902 -0.5937949 0.7180511 0.2410138 ... -0.1487819 -0.4129208\n", + "[3,] -0.17529489 -0.4070562 -1.3540915 -0.1571198 ... -0.6434944 0.6516976\n", + " [,14] [,15] [,16] [,17] [,18] [,19] \n", + "[1,] 0.014388927 1.03903354 -1.32588407 -1.3320434 0.4669779 -0.6306242\n", + "[2,] -0.817239892 -1.22346427 0.37972805 1.1926767 -0.4528030 1.0400085\n", + "[3,] 0.005204321 -0.06921579 0.05620399 0.6300882 -0.6084632 -1.1760778\n", + " [,20] [,21] \n", + "[1,] -1.4325079 -1.60840683\n", + "[2,] 0.7165630 -0.11204069\n", + "[3,] 0.1119728 -0.05696253" ] }, "metadata": {}, @@ -2153,7 +2338,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 30, "metadata": { "vscode": { "languageId": "r" @@ -2179,7 +2364,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 31, "metadata": { "vscode": { "languageId": "r" @@ -2192,7 +2377,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 32, "metadata": { "vscode": { "languageId": "r" @@ -2212,25 +2397,7 @@ }, { "cell_type": "code", - "execution_count": 138, - "metadata": { - "vscode": { - "languageId": "r" - } - }, - "outputs": [], - "source": [ - "nelson_sieguel_rate <- function(insumo,maturity){\n", - " x <- insumo[,1] + insumo[,2]*((1 - exp(-insumo[,4]*maturity))/insumo[,4]*maturity)+insumo[,3]*(((1- exp(-insumo[,4]*maturity))/insumo[,4]*maturity)-exp(-insumo[,4]*maturity))\n", - " x <- as.data.frame(x)\n", - " names(x) <- c('rate')\n", - " return(x)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 142, + "execution_count": 33, "metadata": { "vscode": { "languageId": "r" @@ -2243,7 +2410,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 34, "metadata": { "vscode": { "languageId": "r" @@ -2253,13 +2420,13 @@ { "data": { "text/plain": [ - " rate \n", - " Min. : 11.27 \n", - " 1st Qu.: 11.61 \n", - " Median : 12.95 \n", - " Mean : 24.46 \n", - " 3rd Qu.: 16.57 \n", - " Max. :849.82 " + " rate \n", + " Min. : 3.142 \n", + " 1st Qu.: 4.715 \n", + " Median : 7.684 \n", + " Mean : 11.610 \n", + " 3rd Qu.: 11.615 \n", + " Max. :345.468 " ] }, "metadata": {}, @@ -2268,20 +2435,20 @@ { "data": { "text/plain": [ - " 6m 1y 3y 5y \n", - " Min. :1.946 Min. :2.137 Min. :3.026 Min. :3.888 \n", - " 1st Qu.:4.136 1st Qu.:4.336 1st Qu.:5.118 1st Qu.:5.607 \n", - " Median :4.637 Median :4.739 Median :5.450 Median :6.040 \n", - " Mean :4.617 Mean :4.759 Mean :5.490 Mean :6.058 \n", - " 3rd Qu.:5.289 3rd Qu.:5.368 3rd Qu.:5.853 3rd Qu.:6.449 \n", - " Max. :7.951 Max. :8.104 Max. :9.205 Max. :9.802 \n", - " 10y 15y \n", - " Min. : 4.811 Min. : 5.212 \n", - " 1st Qu.: 6.430 1st Qu.: 6.850 \n", - " Median : 6.815 Median : 7.221 \n", - " Mean : 6.869 Mean : 7.304 \n", - " 3rd Qu.: 7.248 3rd Qu.: 7.726 \n", - " Max. :10.249 Max. :10.284 " + " Date 6m 1y 3y \n", + " Min. :2012-04-27 00:00:00 Min. :1.946 Min. :2.137 Min. :3.026 \n", + " 1st Qu.:2014-10-21 18:00:00 1st Qu.:4.136 1st Qu.:4.336 1st Qu.:5.118 \n", + " Median :2017-04-15 12:00:00 Median :4.637 Median :4.739 Median :5.450 \n", + " Mean :2017-04-15 12:00:00 Mean :4.617 Mean :4.759 Mean :5.490 \n", + " 3rd Qu.:2019-10-09 06:00:00 3rd Qu.:5.289 3rd Qu.:5.368 3rd Qu.:5.853 \n", + " Max. :2022-04-04 00:00:00 Max. :7.951 Max. :8.104 Max. :9.205 \n", + " 5y 10y 15y \n", + " Min. :3.888 Min. : 4.811 Min. : 5.212 \n", + " 1st Qu.:5.607 1st Qu.: 6.430 1st Qu.: 6.850 \n", + " Median :6.040 Median : 6.815 Median : 7.221 \n", + " Mean :6.058 Mean : 6.869 Mean : 7.304 \n", + " 3rd Qu.:6.449 3rd Qu.: 7.248 3rd Qu.: 7.726 \n", + " Max. :9.802 Max. :10.249 Max. :10.284 " ] }, "metadata": {}, @@ -2293,35 +2460,52 @@ "summary(tes_cop)" ] }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## TES UVR" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "i_tes_uvr <- tes_uvr_ns[-nrow(tes_uvr_ns),]\n", - "f_tes_uvr <- tes_uvr_ns[-1,]\n", - "delta_tes_uvr_ns <- f_tes_uvr - i_tes_uvr" - ] - }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ + "name": "stderr", + "output_type": "stream", + "text": [ + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n" + ] + }, + { + "data": { + "image/png": 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AhIuBB4sWFx+vNFECokXgyJl78dCEi4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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ggplot(tes_f_rates, aes(rate))+geom_area(stat = \"bin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "i_tes_uvr <- tes_uvr_ns[-nrow(tes_uvr_ns),]\n", + "f_tes_uvr <- tes_uvr_ns[-1,]\n", + "delta_tes_uvr_ns <- f_tes_uvr - i_tes_uvr" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ " beta_0 beta_1 beta_2 lambda \n", " Min. :-0.03172 Min. :-0.14127 Min. :-0.10238 Min. :3.7 \n", " 1st Qu.: 0.08206 1st Qu.:-0.05854 1st Qu.:-0.01466 1st Qu.:3.7 \n", @@ -2475,16 +2659,6 @@ "uvr_f_rates <- nelson_sieguel_rate(mat_f_ns_tes_uvr,0.5)" ] }, - { - "cell_type": "markdown", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Corporativo a EEUU" - ] - }, { "cell_type": "code", "execution_count": 35, @@ -2753,13 +2927,6 @@ "tes_f_rates <- nelson_sieguel_rate(mat_f_ns_corp_usa,0.5)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## COLCAP y SPX" - ] - }, { "cell_type": "code", "execution_count": 52, @@ -2921,13 +3088,6 @@ "summary(index[,2])" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IPC y TRM" - ] - }, { "cell_type": "code", "execution_count": 108, @@ -3114,7 +3274,427 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Calibración de la curva" + "## Proyección conjunta" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "delta_joint <- apply(joint[,-c(1)],2,function(x){returns(x,method = 'simple')}) \n", + "delta_joint[which(!is.finite(delta_joint))] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " tes_cop_6m tes_cop_1y tes_cop_3y \n", + " Min. :-0.313196 Min. :-0.310856 Min. :-0.2787839 \n", + " 1st Qu.:-0.045226 1st Qu.:-0.042053 1st Qu.:-0.0481881 \n", + " Median : 0.003686 Median : 0.006363 Median :-0.0009985 \n", + " Mean : 0.009997 Mean : 0.009246 Mean : 0.0088413 \n", + " 3rd Qu.: 0.041403 3rd Qu.: 0.037636 3rd Qu.: 0.0451324 \n", + " Max. : 0.495530 Max. : 0.409173 Max. : 0.4403006 \n", + " tes_cop_5y tes_cop_10y tes_cop_15y \n", + " Min. :-0.226516 Min. :-0.121380 Min. :-0.107717 \n", + " 1st Qu.:-0.044270 1st Qu.:-0.029781 1st Qu.:-0.030805 \n", + " Median : 0.000000 Median :-0.004366 Median :-0.005072 \n", + " Mean : 0.008135 Mean : 0.006614 Mean : 0.005613 \n", + " 3rd Qu.: 0.040290 3rd Qu.: 0.036152 3rd Qu.: 0.036079 \n", + " Max. : 0.367116 Max. : 0.255792 Max. : 0.208925 \n", + " tes_uvr_6m tes_uvr_1y tes_uvr_3y \n", + " Min. :-0.157812 Min. :-0.109865 Min. :-0.132972 \n", + " 1st Qu.:-0.030445 1st Qu.:-0.030267 1st Qu.:-0.030730 \n", + " Median :-0.004188 Median :-0.004544 Median :-0.006758 \n", + " Mean : 0.007157 Mean : 0.005810 Mean : 0.005389 \n", + " 3rd Qu.: 0.033468 3rd Qu.: 0.032180 3rd Qu.: 0.032937 \n", + " Max. : 0.293956 Max. : 0.223535 Max. : 0.227046 \n", + " tes_uvr_5y tes_uvr_10y tes_uvr_15y corp_usa_3m \n", + " Min. :-0.148474 Min. :-0.159805 Min. :-0.163527 Min. :-0.64686 \n", + " 1st Qu.:-0.029820 1st Qu.:-0.030523 1st Qu.:-0.031500 1st Qu.:-0.06097 \n", + " Median :-0.004807 Median :-0.006339 Median :-0.006674 Median : 0.02268 \n", + " Mean : 0.005511 Mean : 0.005647 Mean : 0.005700 Mean : 0.03769 \n", + " 3rd Qu.: 0.028370 3rd Qu.: 0.027870 3rd Qu.: 0.027904 3rd Qu.: 0.12019 \n", + " Max. : 0.267774 Max. : 0.298389 Max. : 0.308605 Max. : 1.11498 \n", + " corp_usa_6m corp_usa_1y corp_usa_3y corp_usa_5y \n", + " Min. :-0.58669 Min. :-0.54465 Min. :-0.46585 Min. :-0.36348 \n", + " 1st Qu.:-0.06472 1st Qu.:-0.07168 1st Qu.:-0.06024 1st Qu.:-0.05716 \n", + " Median : 0.01184 Median : 0.01289 Median : 0.01317 Median : 0.00000 \n", + " Mean : 0.03542 Mean : 0.03335 Mean : 0.02351 Mean : 0.01586 \n", + " 3rd Qu.: 0.11088 3rd Qu.: 0.10386 3rd Qu.: 0.08882 3rd Qu.: 0.06897 \n", + " Max. : 1.10651 Max. : 1.04168 Max. : 0.52055 Max. : 0.63533 \n", + " corp_usa_10y corp_usa_15y colcap \n", + " Min. :-0.195668 Min. :-0.138419 Min. :-0.307902 \n", + " 1st Qu.:-0.041788 1st Qu.:-0.035324 1st Qu.:-0.027846 \n", + " Median :-0.008578 Median :-0.006169 Median : 0.008672 \n", + " Mean : 0.005918 Mean : 0.001335 Mean : 0.001076 \n", + " 3rd Qu.: 0.028153 3rd Qu.: 0.023759 3rd Qu.: 0.043341 \n", + " Max. : 0.529892 Max. : 0.343482 Max. : 0.118704 \n", + " spx ipc trm \n", + " Min. :-0.1987059 Min. :-0.006906 Min. :-0.085799 \n", + " 1st Qu.:-0.0003892 1st Qu.: 0.001401 1st Qu.:-0.018069 \n", + " Median : 0.0207628 Median : 0.003871 Median : 0.005586 \n", + " Mean : 0.0164891 Mean : 0.005314 Mean : 0.011119 \n", + " 3rd Qu.: 0.0431170 3rd Qu.: 0.007156 3rd Qu.: 0.031529 \n", + " Max. : 0.1268441 Max. : 0.032239 Max. : 0.256291 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(delta_joint)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "mean_delta_joint = colMeans(delta_joint)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Importance of components:\n", + " PC1 PC2 PC3 PC4 PC5 PC6 PC7\n", + "Standard deviation 3.1726 2.3737 1.5267 1.29148 1.07610 0.83226 0.70800\n", + "Proportion of Variance 0.4376 0.2450 0.1013 0.07252 0.05035 0.03012 0.02179\n", + "Cumulative Proportion 0.4376 0.6826 0.7839 0.85644 0.90679 0.93690 0.95870\n", + " PC8 PC9 PC10 PC11 PC12 PC13 PC14\n", + "Standard deviation 0.58944 0.53964 0.3426 0.30083 0.2091 0.14147 0.12737\n", + "Proportion of Variance 0.01511 0.01266 0.0051 0.00393 0.0019 0.00087 0.00071\n", + "Cumulative Proportion 0.97380 0.98646 0.9916 0.99550 0.9974 0.99827 0.99898\n", + " PC15 PC16 PC17 PC18 PC19 PC20\n", + "Standard deviation 0.10168 0.08478 0.05180 0.04288 0.03365 0.01786\n", + "Proportion of Variance 0.00045 0.00031 0.00012 0.00008 0.00005 0.00001\n", + "Cumulative Proportion 0.99943 0.99974 0.99986 0.99994 0.99999 1.00000\n", + " PC21 PC22 PC23\n", + "Standard deviation 1.695e-15 1.413e-15 9.941e-16\n", + "Proportion of Variance 0.000e+00 0.000e+00 0.000e+00\n", + "Cumulative Proportion 1.000e+00 1.000e+00 1.000e+00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca_joint <- prcomp(delta_joint[, -c(1)], scale=TRUE, center=TRUE)\n", + "summary(pca_joint)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Importance of components:\n", + " PC1 PC2 PC3 PC4 PC5 PC6 PC7\n", + "Standard deviation 0.4419 0.2206 0.1587 0.11715 0.09059 0.05874 0.04549\n", + "Proportion of Variance 0.6482 0.1616 0.0836 0.04557 0.02725 0.01146 0.00687\n", + "Cumulative Proportion 0.6482 0.8098 0.8934 0.93901 0.96625 0.97771 0.98458\n", + " PC8 PC9 PC10 PC11 PC12 PC13 PC14\n", + "Standard deviation 0.03738 0.03188 0.02940 0.02507 0.01597 0.01443 0.008679\n", + "Proportion of Variance 0.00464 0.00337 0.00287 0.00209 0.00085 0.00069 0.000250\n", + "Cumulative Proportion 0.98922 0.99259 0.99546 0.99755 0.99840 0.99909 0.999340\n", + " PC15 PC16 PC17 PC18 PC19 PC20\n", + "Standard deviation 0.007734 0.007478 0.00526 0.005036 0.004197 0.003601\n", + "Proportion of Variance 0.000200 0.000190 0.00009 0.000080 0.000060 0.000040\n", + "Cumulative Proportion 0.999540 0.999720 0.99981 0.999900 0.999960 1.000000\n", + " PC21 PC22 PC23\n", + "Standard deviation 0.0006001 5.154e-06 3.092e-07\n", + "Proportion of Variance 0.0000000 0.000e+00 0.000e+00\n", + "Cumulative Proportion 1.0000000 1.000e+00 1.000e+00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca_joint <- prcomp(delta_joint)\n", + "summary(pca_joint)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_joint$residuals\n", + "X-squared = 0.070259, df = 1, p-value = 0.791\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_2_joint$residuals\n", + "X-squared = 0.0080964, df = 1, p-value = 0.9283\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_3_joint$residuals\n", + "X-squared = 1.0632, df = 1, p-value = 0.3025\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_pca_1_joint <- auto.arima(pca_joint$x[,1],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_1_joint$residuals)\n", + "arima_pca_2_joint <- auto.arima(pca_joint$x[,2],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_2_joint$residuals)\n", + "arima_pca_3_joint <- auto.arima(pca_joint$x[,3],stepwise = T,approximation = F)\n", + "Box.test(arima_pca_3_joint$residuals)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "sim_pca_1_joint <- replicate(expr = simulate(object = arima_pca_1_joint,nsim = future),n = simulations)\n", + "sim_pca_2_joint <- replicate(expr = simulate(object =arima_pca_2_joint,nsim = future),n = simulations)\n", + "sim_pca_3_joint <- replicate(expr = simulate(object =arima_pca_3_joint,nsim = future),n = simulations)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "sim_pca_1_joint_r <- sapply(sim_pca_1_joint,function(x){scale(x%*%t(pca_joint$rotation[,1]), center = -pca_joint$center, scale = F)})\n", + "sim_pca_2_joint_r <- sapply(sim_pca_1_joint,function(x){scale(x%*%t(pca_joint$rotation[,2]), center = -pca_joint$center, scale = F)})\n", + "sim_pca_3_joint_r <- sapply(sim_pca_1_joint,function(x){scale(x%*%t(pca_joint$rotation[,3]), center = -pca_joint$center, scale = F)})" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "sim_pca_1_joint_r <- t(sim_pca_1_joint_r)\n", + "sim_pca_2_joint_r <- t(sim_pca_2_joint_r)\n", + "sim_pca_3_joint_r <- t(sim_pca_3_joint_r)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "sum_sims <- sim_pca_1_joint_r+ sim_pca_2_joint_r+sim_pca_3_joint_r" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "sum_sims_df <- as.data.frame(sum_sims)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " V1 V2 V3 V4 \n", + " Min. :-0.34300 Min. :-0.38008 Min. :-0.53924 Min. :-0.67069 \n", + " 1st Qu.:-0.02194 1st Qu.:-0.02904 1st Qu.:-0.05224 1st Qu.:-0.07237 \n", + " Median : 0.03044 Median : 0.02823 Median : 0.02721 Median : 0.02525 \n", + " Mean : 0.03062 Mean : 0.02843 Mean : 0.02748 Mean : 0.02558 \n", + " 3rd Qu.: 0.08324 3rd Qu.: 0.08595 3rd Qu.: 0.10729 3rd Qu.: 0.12363 \n", + " Max. : 0.36820 Max. : 0.39752 Max. : 0.53953 Max. : 0.65468 \n", + " V5 V6 V7 V8 \n", + " Min. :-0.79302 Min. :-0.83115 Min. :-0.73817 Min. :-0.93865 \n", + " 1st Qu.:-0.09332 1st Qu.:-0.10122 1st Qu.:-0.08429 1st Qu.:-0.11568 \n", + " Median : 0.02083 Median : 0.01787 Median : 0.02239 Median : 0.01859 \n", + " Mean : 0.02122 Mean : 0.01828 Mean : 0.02276 Mean : 0.01905 \n", + " 3rd Qu.: 0.13588 3rd Qu.: 0.13789 3rd Qu.: 0.12991 3rd Qu.: 0.15391 \n", + " Max. : 0.75690 Max. : 0.78575 Max. : 0.71027 Max. : 0.88435 \n", + " V9 V10 V11 V12 \n", + " Min. :-1.15755 Min. :-1.20356 Min. :-1.23747 Min. :-1.24866 \n", + " 1st Qu.:-0.14724 1st Qu.:-0.15333 1st Qu.:-0.15770 1st Qu.:-0.15912 \n", + " Median : 0.01759 Median : 0.01801 Median : 0.01846 Median : 0.01863 \n", + " Mean : 0.01816 Mean : 0.01860 Mean : 0.01907 Mean : 0.01924 \n", + " 3rd Qu.: 0.18371 3rd Qu.: 0.19070 3rd Qu.: 0.19601 3rd Qu.: 0.19779 \n", + " Max. : 1.08043 Max. : 1.12285 Max. : 1.15437 Max. : 1.16482 \n", + " V13 V14 V15 V16 \n", + " Min. :-0.735465 Min. :-0.74781 Min. :-0.78864 Min. :-0.56225 \n", + " 1st Qu.:-0.005069 1st Qu.:-0.01265 1st Qu.:-0.02366 1st Qu.:-0.01757 \n", + " Median : 0.114091 Median : 0.10729 Median : 0.10114 Median : 0.07130 \n", + " Mean : 0.114500 Mean : 0.10770 Mean : 0.10157 Mean : 0.07160 \n", + " 3rd Qu.: 0.234188 3rd Qu.: 0.22817 3rd Qu.: 0.22692 3rd Qu.: 0.16086 \n", + " Max. : 0.882461 Max. : 0.88068 Max. : 0.90589 Max. : 0.64429 \n", + " V17 V18 V19 V20 \n", + " Min. :-0.50734 Min. :-0.37186 Min. :-0.238608 Min. :-0.431287 \n", + " 1st Qu.:-0.02967 1st Qu.:-0.03649 1st Qu.:-0.029772 1st Qu.:-0.065178 \n", + " Median : 0.04826 Median : 0.01823 Median : 0.004299 Median : 0.002647 \n", + " Mean : 0.04853 Mean : 0.01841 Mean : 0.004416 Mean : 0.002416 \n", + " 3rd Qu.: 0.12680 3rd Qu.: 0.07337 3rd Qu.: 0.038637 3rd Qu.: 0.069942 \n", + " Max. : 0.55077 Max. : 0.37103 Max. : 0.223993 Max. : 0.482430 \n", + " V21 V22 V23 \n", + " Min. :0.009428 Min. :-0.005804 Min. :-0.36215 \n", + " 1st Qu.:0.043164 1st Qu.: 0.012914 1st Qu.:-0.02170 \n", + " Median :0.049414 Median : 0.015968 Median : 0.03384 \n", + " Mean :0.049393 Mean : 0.015979 Mean : 0.03403 \n", + " 3rd Qu.:0.055615 3rd Qu.: 0.019046 3rd Qu.: 0.08981 \n", + " Max. :0.093625 Max. : 0.035659 Max. : 0.39198 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(sum_sims_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "sum_sims_df$index <- c(0, rep(1:(nrow(sum_sims_df)-1)%/%260))" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "sum_sims_collapsed <- group_by(sum_sims_df, index) %>%\n", + " summarise_all(list(sum))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "colnames(sum_sims_collapsed)[-c(1)] <- colnames(joint)[-c(1)] " + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " index tes_cop_6m tes_cop_1y tes_cop_3y \n", + " Min. : 0.0 Min. : 0.6428 Min. :-0.6116 Min. :-3.957 \n", + " 1st Qu.:249.8 1st Qu.: 6.5682 1st Qu.: 5.8670 1st Qu.: 5.031 \n", + " Median :499.5 Median : 7.9747 Median : 7.4048 Median : 7.164 \n", + " Mean :499.5 Mean : 7.9624 Mean : 7.3913 Mean : 7.146 \n", + " 3rd Qu.:749.2 3rd Qu.: 9.4489 3rd Qu.: 9.0166 3rd Qu.: 9.400 \n", + " Max. :999.0 Max. :14.5604 Max. :14.6053 Max. :17.154 \n", + " tes_cop_5y tes_cop_10y tes_cop_15y tes_uvr_6m \n", + " Min. :-6.989 Min. :-10.434 Min. :-11.889 Min. :-8.990 \n", + " 1st Qu.: 4.054 1st Qu.: 2.479 1st Qu.: 1.582 1st Qu.: 3.078 \n", + " Median : 6.675 Median : 5.544 Median : 4.780 Median : 5.942 \n", + " Mean : 6.652 Mean : 5.517 Mean : 4.752 Mean : 5.917 \n", + " 3rd Qu.: 9.422 3rd Qu.: 8.757 3rd Qu.: 8.131 3rd Qu.: 8.944 \n", + " Max. :18.948 Max. : 19.896 Max. : 19.752 Max. :19.355 \n", + " tes_uvr_1y tes_uvr_3y tes_uvr_5y tes_uvr_10y \n", + " Min. :-13.809 Min. :-18.3124 Min. :-19.107 Min. :-19.6589 \n", + " 1st Qu.: 1.379 1st Qu.: 0.3334 1st Qu.: 0.276 1st Qu.: 0.2687 \n", + " Median : 4.985 Median : 4.7593 Median : 4.877 Median : 4.9989 \n", + " Mean : 4.953 Mean : 4.7204 Mean : 4.836 Mean : 4.9573 \n", + " 3rd Qu.: 8.763 3rd Qu.: 9.3982 3rd Qu.: 9.699 3rd Qu.: 9.9567 \n", + " Max. : 21.865 Max. : 25.4829 Max. : 26.419 Max. : 27.1472 \n", + " tes_uvr_15y corp_usa_3m corp_usa_6m corp_usa_1y \n", + " Min. :-19.8354 Min. :13.12 Min. :11.24 Min. : 8.968 \n", + " 1st Qu.: 0.2726 1st Qu.:26.60 1st Qu.:24.81 1st Qu.:23.086 \n", + " Median : 5.0456 Median :29.80 Median :28.03 Median :26.437 \n", + " Mean : 5.0036 Mean :29.77 Mean :28.00 Mean :26.408 \n", + " 3rd Qu.: 10.0483 3rd Qu.:33.15 3rd Qu.:31.41 3rd Qu.:29.950 \n", + " Max. : 27.3943 Max. :44.78 Max. :43.11 Max. :42.129 \n", + " corp_usa_3y corp_usa_5y corp_usa_10y corp_usa_15y \n", + " Min. : 6.199 Min. : 1.727 Min. :-2.858 Min. :-3.6129 \n", + " 1st Qu.:16.251 1st Qu.:10.543 1st Qu.: 3.331 1st Qu.: 0.2413 \n", + " Median :18.637 Median :12.635 Median : 4.800 Median : 1.1562 \n", + " Mean :18.616 Mean :12.617 Mean : 4.787 Mean : 1.1481 \n", + " 3rd Qu.:21.138 3rd Qu.:14.828 3rd Qu.: 6.340 3rd Qu.: 2.1151 \n", + " Max. :29.810 Max. :22.433 Max. :11.680 Max. : 5.4398 \n", + " colcap spx ipc trm \n", + " Min. :-7.8487 Min. :12.06 Min. :3.728 Min. : 1.086 \n", + " 1st Qu.:-1.2817 1st Qu.:12.67 1st Qu.:4.073 1st Qu.: 7.369 \n", + " Median : 0.6122 Median :12.84 Median :4.155 Median : 8.860 \n", + " Mean : 0.6281 Mean :12.84 Mean :4.154 Mean : 8.847 \n", + " 3rd Qu.: 2.4192 3rd Qu.:13.01 3rd Qu.:4.241 3rd Qu.:10.423 \n", + " Max. :10.0319 Max. :13.71 Max. :4.539 Max. :15.843 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(sum_sims_collapsed)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Calibración de la curva" ] }, { @@ -3181,6 +3761,237 @@ "source": [ "1- gamma" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Restricción del activo" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Installing package into 'C:/Users/Diana C Contreras/Documents/R/win-library/3.6'\n", + "(as 'lib' is unspecified)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "package 'quadprog' successfully unpacked and MD5 sums checked\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning message:\n", + "\"cannot remove prior installation of package 'quadprog'\"Warning message in file.copy(savedcopy, lib, recursive = TRUE):\n", + "\"problema al copiar C:\\Users\\Diana C Contreras\\Documents\\R\\win-library\\3.6\\00LOCK\\quadprog\\libs\\x64\\quadprog.dll a C:\\Users\\Diana C Contreras\\Documents\\R\\win-library\\3.6\\quadprog\\libs\\x64\\quadprog.dll: Permission denied\"Warning message:\n", + "\"restored 'quadprog'\"" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "The downloaded binary packages are in\n", + "\tC:\\Users\\Diana C Contreras\\AppData\\Local\\Temp\\Rtmpmaq3rz\\downloaded_packages\n" + ] + } + ], + "source": [ + "install.packages('quadprog')" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "library('quadprog')" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "varcov <- cov(sum_sims_collapsed[,c(2:22)])" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
tes_cop_6m
7.96235531742021
tes_cop_1y
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tes_cop_3y
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tes_cop_5y
6.65164350801705
tes_cop_10y
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tes_cop_15y
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tes_uvr_6m
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tes_uvr_1y
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tes_uvr_3y
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tes_uvr_5y
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tes_uvr_10y
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tes_uvr_15y
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corp_usa_3m
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corp_usa_6m
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corp_usa_1y
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corp_usa_3y
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corp_usa_5y
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corp_usa_10y
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corp_usa_15y
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colcap
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spx
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  1. 1
  2. -1
\n" + ], + "text/latex": [ + "\\begin{enumerate*}\n", + "\\item 1\n", + "\\item -1\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. 1\n", + "2. -1\n", + "\n", + "\n" + ], + "text/plain": [ + "[1] 1 -1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "one<- matrix(1, nrow = 1, ncol = 21) #Matrix that defines the \"greater or equal to\" restriction. \n", + "minusone<- matrix(0, nrow = 1, ncol = 21) #Matrix that defines \"less or equal to\" restriction\n", + "sumone <- rbind(one, minusone) #Matrix of constraints of x \n", + "Amat <- sumone\n", + "b0 <- c(1,-1) # Lower bounds\n", + "b0" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "fn3<- function(phi){\n", + " res <- solve.QP(Dmat=varcov, dvec=1/phi*returns, Amat=t(Amat), bvec=b0)\n", + " return(res$solution)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "ename": "ERROR", + "evalue": "Error in solve.QP(Dmat = varcov, dvec = 1/phi * returns, Amat = t(Amat), : matrix D in quadratic function is not positive definite!\n", + "output_type": "error", + "traceback": [ + "Error in solve.QP(Dmat = varcov, dvec = 1/phi * returns, Amat = t(Amat), : matrix D in quadratic function is not positive definite!\nTraceback:\n", + "1. fn3(0.424283357506555)", + "2. solve.QP(Dmat = varcov, dvec = 1/phi * returns, Amat = t(Amat), \n . bvec = b0) # at line 2 of file ", + "3. stop(\"matrix D in quadratic function is not positive definite!\")" + ] + } + ], + "source": [ + "fn3(0.424283357506555)" + ] } ], "metadata": {