Structural Default Prediction Engine (Merton Model)

📌 Project Overview

This project implements a Structural Credit Risk Model based on the Merton (1974) framework. Unlike traditional accounting-based models (Altman Z-Score), this model treats a company's Equity as a Call Option on its Assets (strike price = debt liabilities).

By reverse-engineering the Black-Scholes-Merton formula, this engine solves for unobservable Asset Value and Asset Volatility to calculate a market-implied Distance to Default (DD) and Probability of Default (PD).

🚀 Key Features

• Reverse Engineering: Solves the non-linear system of equations linking Equity Volatility to Asset Volatility.
• Dynamic Debt Barriers: Automatically parses Balance Sheets to construct the KMV-style default barrier ($Short Term Debt + 0.5 \times Long Term Debt$).
• Automated Data Pipeline: Fetches real-time equity data and risk-free rates (Treasury Yields) via yfinance.
• Production-Grade Code: Includes robust error handling, logging, and modular architecture.

🛠️ Tech Stack

• Language: Python 3.x
• Libraries: scipy.optimize (fsolve), numpy (Numerical integration), pandas, yfinance
• Math: Stochastic Calculus (Ito's Lemma), Black-Scholes Pricing Model

🧮 The Mathematics

The model solves the following system simultaneously:

$$E = V_A N(d_1) - D e^{-rT} N(d_2)$$ $$\sigma_E = \frac{V_A}{E} N(d_1) \sigma_A$$

Where:

• $E$: Market Value of Equity (Observable)
• $\sigma_E$: Equity Volatility (Observable)
• $D$: Debt Face Value (Balance Sheet)
• $V_A$: Asset Value (Solved for)
• $\sigma_A$: Asset Volatility (Solved for)

📜 Disclaimer

This tool is for educational and research purposes only. It does not constitute financial advice.