Estimating multifactor portfolio credit risk: A variance reduction approach

Hsieh, Ming‐Hua; Lee, Yi-Hsi; Shyu, So-De; Chiu, Yu-Fen

doi:10.1016/j.pacfin.2018.08.001

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…For banks, when the asset portfolio is a loan portfolio, then banks can simply retain reasonable funds to meet the provision coverage ratio without excessively placing idle funds, thereby maximizing the bank's profitability while satisfying its risk preparation. The corresponding research on estimating firms' credit portfolio risk has been emphasized in both academia and industries (Hailemariam, Hill, & Demissie, 2012;Boudreault, Gauthier, & Thomassin, 2015;Chen, Zhou, Jin, & Zheng, 2018;Hsieh, Lee, Shyu, & Chiu, 2018;Osmundsen, 2018;Bülbül, Hakenes, & Lambert, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Information, Incentives, and Effects of Risk-Sharing on the Real Economy

Liu

2018

SSRN Journal

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Implementation of variance reduction techniques applied to the pricing of investment certificates

Bottasso¹,

Fusaro²,

Giribone³

et al. 2023

RMM

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Certificates are structured financial instruments that aim to provide investors with investment solutions tailored to their needs. Certificates can be modeled using a bond component and a derivative component, typically an options strategy. The pricing of certificates is typically performed using the Monte Carlo numerical methodology. Such method allows for projections of the underlying using series of random numbers. The results obtained display an error (standard deviation) that depends on the number of simulations used and on the specific characteristics of the structured product. This work has the objective of minimizing the experimental error, and, consequently, of accelerating the speed of convergence using statistical techniques known in the literature as variance reduction methods. The most popular stochastic dynamics have been analyzed, like the classical Black and Scholes model, the Local Volatility model and the Heston model. Three certificates are analyzed in the paper and they are characterized by different payoffs. The variance reduction techniques, implemented in different programming languages (Python, Matlab and R), are: Latin Hypercube, Stratified Sampling, Antithetic Variables, Importance Sampling, Moment Matching and Control Variates

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Estimating multifactor portfolio credit risk: A variance reduction approach

Cited by 3 publications

References 14 publications

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

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

Information, Incentives, and Effects of Risk-Sharing on the Real Economy

Implementation of variance reduction techniques applied to the pricing of investment certificates

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