A Hybrid Importance Sampling Algorithm for Value-at-Risk

Value-at-risk (VaR) provides a number that measures the risk of a financial portfolio under significant loss. Glasser- man et al. suggest that the performance of Mote Calo simulation can be improved by importance sampling. However, their technique might perform poorly for some complex portfolios like shorting straddle options. In this paper, we investigate the hybrid importance sampling algorithm which can efficiently estimate the VaR for complex portfolios.