Market-based KMV/Merton credit-rating pipeline for public companies: EM asset estimation, PIT/TTC probability of default, and S&P-equivalent ratings.

Bank default risk analysis using Merton's Distance-to-Default model. Computes PD scores from market and balance sheet data with solver validation and logging. Built in Python.

Credit risk from first principles: Merton structural model (equity = call on assets, debt three independent ways), hazard/intensity survival, CDS legs + bootstrap, defaultable bonds. Every formula identity-tested — the credit triangle emerges, never echoed. No pricing library underneath.

Distressed-corporate turnaround simulator merging Merton distance-to-default, Altman Z-score and Bayesian lender belief into a 12-quarter decision engine. Python · Streamlit · Plotly.

Dynamic credit-portfolio management under macroeconomic conditions — Merton PD, econometric forecasting, portfolio optimization, backtesting (MSc thesis).

A structural credit risk engine implementing the Merton (1974) model. Reverse-engineers Black-Scholes to calculate Distance-to-Default (DD) and Implied Default Probabilities (PD) using market equity data and balance sheet structures.

An advanced Python framework for pricing financial derivatives beyond Black-Scholes using the Heston Stochastic Volatility Model and the Merton Jump Diffusion Model. The project evaluates European, American, and Barrier options, analyzes strike sensitivities, and computes Greeks using Monte Carlo simulations.

📊 Institutional Financial Ratios & Merton Credit Risk Default Engine

Developed a quantitative credit risk assessment framework using Merton's Default Probability Model, Minsky's Financial Instability Hypothesis, and Markov Chains to classify companies into risk categories and analyze long-term investment risk.

Merton portfolio optimization with a Wishart-process covariance (Dyson eigenvalue repulsion / RMT), solved via matrix Riccati and a Deep BSDE. 170 tests + CI.