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

Ming-Chin Hung,Shih-Kuei Lin,Yung-Kang Ching

doi:10.3390/math9233087

Ming-Chin Hung, Shih-Kuei Lin + Show 1 more

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https://doi.org/10.3390/math9233087

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Journal: Mathematics	Publication Date: Nov 30, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: Soochow University, National Chengchi University

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.

Highlights

Credit risk is generally understood as the potential that a borrower or counterparty will fail to meet its contractual obligations
Motivated by observing the fluctuated credit impairment estimation and credit cost percentage exhibited in Figures 1 and 2, the main aim of this paper is to explore the robustness of the probability of default (PD) model with a Gross domestic product (GDP) determinant
We explore the usage of a credit default swap index (CDX) determinant in a PD model in place of a GDP determinant to reach a less volatile credit loss estimation

Summary

Introduction

Credit risk is generally understood as the potential that a borrower or counterparty will fail to meet its contractual obligations. Banks need to estimate the probability of such events occurring and set aside capital to absorb contingent losses. Loan loss provision estimates are constantly updated based on the bank’s potential customer defaults. These estimates are usually calculated based on a probability of default (PD) model, as applied to historical default data. Credit risk evaluation is crucial for internal credit decisions and for regulatory purposes (BCBS [1,2]).

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