merton-model

European option pricing under Black-Scholes-Merton: prices, Greeks, implied volatility, and sensitivity surfaces. Typed, tested, NumPy-only — no SciPy, so it runs in a browser. The published document shows where the model breaks, generating a volatility smile and inverting it.

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.

KMV/Merton structural credit ratings with quantified uncertainty — 150-name universe, bootstrap-validated, benchmarked against agency ratings. Live terminal + Python pipeline.

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

Portfolio Credit VaR across 3 methodologies — CreditMetrics (rating migration), KMV/Merton (structural default), and Basel IRB (regulatory formula) — with a side-by-side comparison notebook.

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

📊 Institutional Financial Ratios & Merton Credit Risk Default Engine

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.

Portfolio performance, risk decomposition, and corporate credit assessment on live market data and SEC filings

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.

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.

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.

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.