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Stress Testing with a Bottom-Up Corporate Default Prediction Model

Stress testing plays an important role in risk management by focusing on extreme events that are rare but bear significance. This paper proposes an operational system for credit stress testing which is comprised of (1) the daily updated corporate default prediction system maintained by the Risk Management Institute at National University of Singapore, and (2) a set of stress-testing regressions translating the presumed adverse macroeconomic scenarios into the input values for the default prediction system. Our credit stress testing system produces distributions of corporate defaults for an economy and/or any target portfolio over typical horizons of interest. The current model powering the RMI system employs a forward intensity-based corporate default prediction model and forms a bottom-up approach to aggregating individual obligors’ behaviours into a portfolio’s default profile. Each stress-testing regression relates an input variable to a common set of macroeconomic variables that define stress scenarios. We focus on the Singapore economy in the empirical analysis but also use the US to illustrate its wider applicability. We find that the eight macroeconomic stress variables used in the analysis can explain a large fraction of variations in the input variables of the corporate default prediction model. We also demonstrate that the proposed stress testing system performs well.