Computing risk measures by importance sampling
Seminar Room 1, Newton Institute
AbstractComputation of extreme quantiles and tail-based risk measures using standard Monte Carlo simulation can be inefficient. A method to speed up computations is provided by importance sampling. We show that importance sampling algorithms, designed for e¢ cient tail probability estimation, can signi.cantly improve Monte Carlo estimators of tail-based risk measures. In the heavy-tailed setting, when the random variable of interest has a regularly varying distribution, we provide su¢ cient conditions for the asymptotic relative error of importance sampling estimators of risk measures, such as Value-at-Risk and expected shortfall, to be small. The results are illustrated by some numerical examples.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.