Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are two widely-used measures in risk management. This paper deals with the problem of estimating both VaR and CVaR using stochastic approximation (with decreasing steps): we propose a first Robbins-Monro (RM) procedure based on Rockafellar- Uryasev's identity for the CVaR. The estimator provided by the algorithm satisfies a Gaussian Central Limit Theorem. As a second step, in order to speed up the initial procedure, we propose a recursive and adaptive importance sampling (IS) procedure which induces a significant variance reduction of both VaR and CVaR procedures. This idea, which has been investigated by many authors, follows a new approach introduced in Lemaire and Pagès [20]. Finally, to speed up the initialization phase of the IS algorithm, we replace the original confidence level of the VaR by a deterministic moving risk level.We prove that the weak convergence rate of the resulting procedure is ruled by a Central Limit Theorem with minimal variance and we illustrate its efficiency by considering typical energy portfolios. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Bardou, O., Frikha, N., & Pagès, G. (2009). Recursive computation of value-at-risk and conditional value-at-risk using MC and QMC. In Monte Carlo and Quasi-Monte Carlo Methods 2008 (pp. 193–208). Springer Verlag. https://doi.org/10.1007/978-3-642-04107-5_11
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