A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*

Abstract

In this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t-copula. Moreover, we develop a three-step importance sampling (IS) method for the t-copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg-Marquardt algorithm to estimate the mean-shift vector of the systematic risk factors after the probability measure change. Besides, we empirically * The authors would like to thank editors and three referees for valuable suggestions and helpful comments.

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“…Considering the developing Fintech and data analytic technologies, Chen et al (2020b) it can be reasonably hypothesized the market should be very efficient and positively react to the real peak of COVID-19 in any efficient market. Suggested by the cumulative abnormal returns (CAR), we find that all cross-country markets (Australia, China, Germany, India, Japan, the UK and US) significantly react to COVID-19 when a relative long event window ([−30, 30] days) is used.…”

The Moderating Role BOPO of CAR, NPL, NIM and LDR on Banking Stock Returns on the Indonesian Stock Exchange

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Is the hybrid method more adequate for measuring operational risk?

“…Measurement techniques or models that can determine the capital burden of operational risk are indeed needed in a healthy organizational risk management process (Chen et al, 2022). These models help companies reduce capital requirements, allowing excess money to be used elsewhere to acquire more profitable investments while maintaining exposure to risks that could affect future revenue-generating capabilities (Torre-Enciso & Barros, 2013).…”