diff --git a/Taller 2/Scripts/first.ipynb b/Taller 2/Scripts/first.ipynb new file mode 100644 index 0000000..a0dabdc --- /dev/null +++ b/Taller 2/Scripts/first.ipynb @@ -0,0 +1,4017 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Optimización del Activo\n", + "## Import de paquetes y funciones" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "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\"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", + "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", + " first, last\n", + "\n", + "The following objects are masked from 'package:stats':\n", + "\n", + " filter, lag\n", + "\n", + "The following objects are masked from 'package:base':\n", + "\n", + " intersect, setdiff, setequal, union\n", + "\n" + ] + } + ], + "source": [ + "library(\"readxl\")\n", + "library(\"lubridate\")\n", + "library('tidyr')\n", + "library('ggplot2')\n", + "library('YieldCurve')\n", + "library('dplyr')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
  1. '.GlobalEnv'
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  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", + "\\item 'package:ggplot2'\n", + "\\item 'package:tidyr'\n", + "\\item 'package:lubridate'\n", + "\\item 'package:readxl'\n", + "\\item 'jupyter:irkernel'\n", + "\\item 'jupyter:irkernel'\n", + "\\item 'package:stats'\n", + "\\item 'package:graphics'\n", + "\\item 'package:grDevices'\n", + "\\item 'package:utils'\n", + "\\item 'package:datasets'\n", + "\\item 'package:methods'\n", + "\\item 'Autoloads'\n", + "\\item 'package:base'\n", + "\\end{enumerate*}\n" + ], + "text/markdown": [ + "1. '.GlobalEnv'\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. '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: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": {}, + "output_type": "display_data" + } + ], + "source": [ + "search()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "source(\"helpers.R\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "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
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\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": 38, + "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", + " k <- i-1\n", + " for (j in 1:future){\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", + " }\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": 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" + } + }, + "outputs": [], + "source": [ + "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", + "}\n" + ] + }, + { + "cell_type": "code", + "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
X1X2X3X4X5X6X7X8X9X10...X22X23X24X25X26X27X28X29X30X31
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............................................................
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6e+05583620566804549239531321512720493821474900456018437061...228312213810199687185917172914160141147597135614124002113074
\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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"execution_count": 42, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# install.packages('data.table')\n", + "library('data.table')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "sims$id <- seq(1, 1000, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "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": 45, + "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
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\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": 46, + "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": 34, + "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": 47, + "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": 48, + "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": 49, + "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": {}, + "source": [ + "## 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." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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": "markdown", + "metadata": {}, + "source": [ + "### Empezamos con los bonos TES en pesos." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "tes_cop <- read_excel(path, sheet=1,skip=7, col_names=c('Date','6m','1y','3y','5y','10y','15y'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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
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[,-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": {}, + "outputs": [], + "source": [ + "tes_maturity <- c(0.5,1,3,5,10,15)" + ] + }, + { + "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": 6, + "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 × 5
Datebeta_0beta_1beta_2lambda
<dttm><dbl><dbl><dbl><dbl>
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 × 5\n", + "\\begin{tabular}{r|lllll}\n", + " & Date & beta\\_0 & beta\\_1 & beta\\_2 & lambda\\\\\n", + " & & & & & \\\\\n", + "\\hline\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 × 5\n", + "\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": [ + " 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": {}, + "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": "code", + "execution_count": 7, + "metadata": {}, + "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": 8, + "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 × 7
tes_uvr_6mtes_uvr_1ytes_uvr_3ytes_uvr_5ytes_uvr_10ytes_uvr_15yDate
<dbl><dbl><dbl><dbl><dbl><dbl><dttm>
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", + " & tes\\_uvr\\_6m & tes\\_uvr\\_1y & tes\\_uvr\\_3y & tes\\_uvr\\_5y & tes\\_uvr\\_10y & tes\\_uvr\\_15y & Date\\\\\n", + " & & & & & & & \\\\\n", + "\\hline\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", + "| | 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", + "| 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": [ + " 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": {}, + "output_type": "display_data" + } + ], + "source": [ + "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", + "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": 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 × 8
Datecorp_usa_3mcorp_usa_6mcorp_usa_1ycorp_usa_3ycorp_usa_5ycorp_usa_10ycorp_usa_15y
<dttm><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
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 × 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", + "\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 × 8\n", + "\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": [ + " 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": {}, + "output_type": "display_data" + } + ], + "source": [ + "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": { + "tags": [] + }, + "source": [ + "### ETF's de seguimiento de la economía colombiana (COLCAP) y estadounidense (SPX)" + ] + }, + { + "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 data.frame: 6 × 3
Datecolcapspx
<dttm><dbl><dbl>
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 × 3\n", + "\\begin{tabular}{r|lll}\n", + " & Date & colcap & spx\\\\\n", + " & & & \\\\\n", + "\\hline\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 × 3\n", + "\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": [ + " 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", + "index <- as.data.frame(index)\n", + "head(index)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Indice de Precios al consumidor" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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 × 2
Dateipc
<dttm><dbl>
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 data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & Date & ipc\\\\\n", + " & & \\\\\n", + "\\hline\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 data.frame: 6 × 2\n", + "\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": [ + " 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": [ + "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": { + "tags": [] + }, + "source": [ + "### Tasa de cambio" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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 × 2
Datetrm
<dttm><dbl>
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 data.frame: 6 × 2\n", + "\\begin{tabular}{r|ll}\n", + " & Date & trm\\\\\n", + " & & \\\\\n", + "\\hline\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 data.frame: 6 × 2\n", + "\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": [ + " 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", + "trm <- as.data.frame(trm)\n", + "head(trm)" + ] + }, + { + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Set up " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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": 16, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "library('stats')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": "code", + "execution_count": 18, + "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": 18, + "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": 19, + "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": 20, + "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": 21, + "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$x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seleccionamos los primeros 2 componentes que explican el 95% del comportamiento de la serie y corremos un arima para cada uno de ellos" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\tBox-Pierce test\n", + "\n", + "data: arima_pca_1_tes_cop$residuals\n", + "X-squared = 0.018292, 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$residuals\n", + "X-squared = 0.018292, df = 1, p-value = 0.8924\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "arima_pca_1_tes_cop <- auto.arima(pca_tes_cop$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$residuals)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulamos a 260 periodos en el futuro (1 año) y realizamos el mismo procedimiento 1000 veces" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "sim_pca_1_tes_cop <- replicate(expr = simulate(object = arima_pca_1_tes_cop,nsim = future),n = simulations)\n", + "sim_pca_2_tes_cop <- replicate(expr = simulate(object =arima_pca_2_tes_cop,nsim = future),n = simulations)\n", + "Bt1 <- pca_tes_cop$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": 29, + "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
-0.1856780-0.1778925-1.02601113-1.92734556-0.3000903 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 -0.1856780 & -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", + "| -0.1856780 | -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,] -0.1856780 -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": {}, + "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": 30, + "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": 31, + "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": 32, + "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": 33, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "tes_f_rates <- nelson_sieguel_rate(mat_f_ns_tes_cop,0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + " 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": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " 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": {}, + "output_type": "display_data" + } + ], + "source": [ + "summary(tes_f_rates)\n", + "summary(tes_cop)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "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", + " 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": {}, + "output_type": "display_data" + } + ], + "source": [ + "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": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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": {}, + "output_type": "display_data" + } + ], + "source": [ + "pca_tes_uvr <- prcomp(delta_tes_uvr_ns[,1:3], scale=TRUE)\n", + "summary(pca_tes_uvr)" + ] + }, + { + "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": "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": "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": "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": [ + "## 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" + ] + }, + { + "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" + ] + }, + { + "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
7.39126270628383
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
4.95283645525138
tes_uvr_3y
4.72040369433642
tes_uvr_5y
4.83638849720991
tes_uvr_10y
4.95732223240105
tes_uvr_15y
5.00363028696876
corp_usa_3m
29.7700709271298
corp_usa_6m
28.0029267606339
corp_usa_1y
26.407874336002
corp_usa_3y
18.616053094326
corp_usa_5y
12.6168112983955
corp_usa_10y
4.78749964998109
corp_usa_15y
1.14813061338281
colcap
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spx
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  1. 1
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\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": { + "interpreter": { + "hash": "55e5e45f2c7483bd6dc7929c8833acb9fd534bb579ffc2e2f3d3d3e99b23c691" + }, + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Taller 2/data/Mortality.xlsx b/Taller 2/data/Mortality.xlsx new file mode 100644 index 0000000..a4ebe9f Binary files /dev/null and b/Taller 2/data/Mortality.xlsx differ diff --git a/Taller 2/data/Datos Insumo (1).xlsx b/Taller 2/data/data.xlsx similarity index 57% rename from Taller 2/data/Datos Insumo (1).xlsx rename to Taller 2/data/data.xlsx index 83f66f3..72174fb 100644 Binary files a/Taller 2/data/Datos Insumo (1).xlsx and b/Taller 2/data/data.xlsx differ