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Title: A study on operational risk and credit portfolio risk estimation using data analytics
Authors: Chen, R
Wang, Z
Yang, L
Ng, CT 
Cheng, TCE 
Issue Date: Feb-2022
Source: Decision sciences, Feb. 2022, v. 53, no. 1, p. 84-123
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 examine the changes in the credit portfolio risks of 60 listed Chinese firms in different industries using our proposed method. The results show that our model can help the decision maker make the optimal operational decisions with total provision for risk, which hedges against the potential risk in the entire supply chain.
Keywords: Credit Portfolio Risk
Data Analytics
Decision Making
Operational Risk Management
Simulation
Publisher: Wiley-Blackwell
Journal: Decision sciences 
ISSN: 0011-7315
EISSN: 1540-5915
DOI: 10.1111/deci.12473
Rights: © 2020 Decision Sciences Institute
This is the peer reviewed version of the following article: Chen, R., Wang, Z., Yang, L., Ng, C.T. and Cheng, T.C.E. (2022), A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*. Decision Sciences, 53: 84-123, which has been published in final form at https://doi.org/10.1111/deci.12473. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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