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Efficient Monte Carlo methods for convex risk measures in portfolio credit risk models

Jörn Dunkel, Stefan Weber

Open publisher page 18 citations

Abstract

We discuss efficient Monte Carlo (MC) methods for the estimation of convex risk measures within the portfolio credit risk model CreditMetrics. Our focus lies on the Utilitybased Shortfall Risk (SR) measures, as these avoid several deficiencies of the current industry standard Value-at-Risk (VaR). It is demonstrated that the importance sampling method exponential twisting provides computationally efficient SR estimators. Numerical simulations of test portfolios illustrate the good performance of the proposed algorithms.

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What this paper is about

We discuss efficient Monte Carlo (MC) methods for the estimation of convex risk measures within the portfolio credit risk model CreditMetrics. Our focus lies on the Utilitybased Shortfall Risk (SR) measures, as these avoid several deficiencies of the current industry standard Value-at-Risk (VaR). It is demonstrated that the importance sampling method exponential twisting provides computationally efficient SR estimators. Numerical simulations of test portfolios illustrate the good performance of the proposed algorithms.

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OpenAlex reports 18 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

We discuss efficient Monte Carlo (MC) methods for the estimation of convex risk measures within the portfolio credit risk model CreditMetrics. Our focus lies on the Utilitybased Shortfall Risk (SR) measures, as these avoid several deficiencies of the current industry standard Value-at-Risk (VaR). It is demonstrated that the importance sampling method exponential twisting provides computationally efficient SR estimators. Numerical simulations of test portfolios illustrate the good performance of the proposed algorithms.

Key concepts: Monte Carlo method, Portfolio, Expected shortfall, Estimator, Computer science, Importance sampling, Value at risk, Mathematical optimization

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