BCR Approach with Python for Low-Default Portfolios

Access the full Python code and input data here: [https://drive.google.com/drive/folders/1T8clLUy9h42pn-WZ9Z89f3Bo98uQb1U4?usp=sharing] 

Key features of the Python implementation:

• Inverse Vasicek calibration using root_scalar() to estimate PD
•  Time-series PD projection based on GDP path volatility
• Goodness-of-fit validation using Binomial hypothesis testing
• Bayesian posterior estimation of PD with credible intervals
• Stress scenario simulation – evaluates PD under adverse GDP shocks
• Sensitivity analysis – assesses how varying asset correlation (ρ) affects PD outcomes