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.

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.

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.

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.

Optimal continuous-time portfolio rebalancing using the HJB equation and Merton Proportion — MATLAB Monte Carlo simulation

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.

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

Simulation and visualization of the classical Merton portfolio problem, connecting stochastic optimization with portfolio allocation.

Credit, earnings-quality and valuation analysis that computes every figure: Altman and Ohlson reconciled into one verdict, Merton default risk from share prices, DCF with Monte Carlo, a six-method Portfolio Lab with HRP, and a multi-agent research memo whose every number is checked.

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.

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.

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

Merton-approach multi-year credit risk engine for an insurer's general account — corporate bonds, commercial mortgages and structured tranches on one persistent credit cycle; NAIC RBC capital path, ES capital allocation, break-even pricing, limits, backtests and model-risk documentation

Which default score flags first: Altman, Ohlson, or Merton, measured on real European credit events

📊 Institutional Financial Ratios & Merton Credit Risk Default Engine

Distance-to-default and implied PD via the structural Merton model

Corporate credit risk platform: EDGAR panel, PD scorecard/ML/Merton, portfolio risk, IFRS 9 ECL, Streamlit dashboard

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

NGFS Phase V transition scenarios to corporate PDs and expected loss (Merton, Pillar 3-calibrated), Italy and Germany 2025-2050. Public data only.