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

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.