Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model

Ming-Chin Hung, Yung-Kang Ching and Shih-Kuei Lin
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Ming-Chin Hung: Department of Financial Engineering and Actuarial Mathematics, Soochow University, Taipei 100, Taiwan
Yung-Kang Ching: Risk Management Development, China Development Financial Holding, Taipei 105, Taiwan
Shih-Kuei Lin: Department of Money and Banking, National Chengchi University, Taipei 116, Taiwan

Mathematics, 2021, vol. 9, issue 23, 1-13

Abstract: Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, PD models with a GDP covariate may not perform well under aberrant (i.e., outlier) conditions such as the COVID-19 pandemic. This study explored the robustness of a PD model with a GDP determinant (the test model) in comparison with that of a PD model with a credit default swap index (CDX) determinant (the alternative model). The test model had a significantly greater ratio of increase in Akaike information criterion than the alternative model in comparisons of the fit performance of models including 2020 data with that of models excluding 2020 data (i.e., that do not cover the COVID-19 pandemic). Furthermore, the Cook’s distance of the 2020 data of the test model was significantly greater than that of the alternative model. Therefore, the test model exhibited a serious robustness issue in outlier scenarios, such as the COVID-19 pandemic, whereas the alternative model was more robust. This finding opens the prospect for the CDX to potentially serve as an alternative to GDP in PD estimation models.

Keywords: Anscombe’s quartet; Cook’s distance; default rate; expected credit loss; gross domestic product (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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