STRESS TESTING CREDIT PORTFOLIOS UNDER MACROECONOMIC SHOCKS

Abstract

This study investigates the impact of macroeconomic shocks on credit portfolios using a structured, scenario-based stress testing framework. Drawing from the empirical methodology developed by Nazmuz Sakib (2021), the paper simulates three economic conditions, baseline, adverse, and severely adverse—based on key macroeconomic indicators such as GDP growth, unemployment, inflation, and interest rates. Credit risk is analyzed across retail, SME, and corporate lending segments using adjusted probability of default (PD), loss given default (LGD), and exposure at default (EAD). The findings reveal that economic deterioration significantly increases expected losses, particularly in SME and corporate segments, underscoring the importance of segment-specific modeling. The study also highlights the role of dynamic LGD assumptions in accurately capturing risk under stressed conditions. By integrating real-world data and offering spreadsheet-compatible outputs, the methodology promotes accessibility and replicability for institutions with limited analytical resources. The research contributes to the growing literature on macroprudential surveillance and provides a practical tool for financial institutions aiming to enhance capital adequacy and risk preparedness. Future research directions include the incorporation of macroeconometric modeling and machine learning techniques to further strengthen the predictive power of stress testing frameworks.