A general framework of importance sampling for value-at-risk and conditional value-at-risk

Authors:
Lihua Sun;L. Jeff Hong
Affiliations:
The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China;The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
Venue:
Winter Simulation Conference
Year:
2009

Citing 3
Cited 0

Variance Reduction Techniques for Estimating Value-at-Risk

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Uniformly Efficient Importance Sampling for the Tail Distribution of Sums of Random Variables

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Simulating Sensitivities of Conditional Value at Risk

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Abstract

Value-at-risk (VaR) and conditional value-at-risk (CVaR) are important risk measures. Importance sampling (IS) is often used to estimate them. We derive the asymptotic representations for IS estimators of VaR and CVaR. Based on these representations, we are able to give simple conditions under which the IS estimators have smaller asymptotic variances than the ordinal estimators. We show that the exponential twisting can yield an IS distribution that satisfies the conditions for both the IS estimators of VaR and CVaR. Therefore, we may be able to estimate VaR and CVaR accurately at the same time.