4.5 Article

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

Journal

DECISION SCIENCES
Volume 53, Issue 1, Pages 84-123

Publisher

WILEY
DOI: 10.1111/deci.12473

Keywords

Credit Portfolio Risk; Data Analytics; Decision Making; Operational Risk Management; Simulation

Categories

Funding

  1. key program of the National Natural Science Foundation of China [71631005]
  2. National Natural Science Foundation of China [71571043, 71471161]
  3. Fundamental Research Funds for the Central Universities in UIBE [CXTD11-04]
  4. University of International Business and Economics [17JQ06]
  5. Hong Kong Polytechnic University [G-YBEF]

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This article uses a new structural credit model integrated with data analytics to estimate and analyze the risk of a credit portfolio. The results show that the model can assist decision makers in making optimal operational decisions with total provision for risk.
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 multivariatet-copula. Moreover, we develop a three-step importance sampling (IS) method for thet-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.

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