BACKTESTING ANALYSIS FOR PROBABILITY OF DEFAULT MODEL

Credit risk is a paramount concern for financial institutions, accurate measurement and management of credit risk are not merely matters of prudent business practice, but are also fundamental requirements stipulated by international regulatory frameworks, such as Basel Accords. At the heart of effective credit risk management lies the development and application of robust internal models designed to estimate key risk parameters, most notably the Probability of Default (PD), Loss Given Default (LGD), and Exposure At Default (EAD). These parameters directly feed into the calculation of capital requirements, risk-based pricing, and overall portfolio management strategies. However, the efficacy and reliability of these complex quantitative models are not self-evident. They are based on assumptions, historical data, and statistical methodologies that must be empirically validated against real-world outcomes. This is where backtesting emerges as an indispensable tool. Theoretically, backtesting serves as the critical feedback mechanism in the model life-cycle, transforming model development from a purely theoretical exercise into an iterative process of continuous improvement and validation. Credit risk backtesting is far more than a statistical exercise; it is an organizational imperative that bridges the gap between theoretical model construction and practical, real-world performance. Ensures the ongoing validity, reliability, and regulatory compliance of credit risk models, thus underpinning sound capital management and fostering financial stability.