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SUMMARY:Computing risk measures by importance sampling - Hult\, H\, Svenss
 on\, J (KTH\, Stockholm)
DTSTART:20100621T130000Z
DTEND:20100621T135000Z
UID:TALK25315@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Computation of extreme quantiles and tail-based risk measures 
 using standard Monte Carlo simulation can be inefficient. A method to spee
 d up computations is provided by importance sampling. We show that importa
 nce sampling algorithms\, designed for e cient tail probability estimation
 \, can signi.cantly improve Monte Carlo estimators of tail-based risk meas
 ures. In the heavy-tailed setting\, when the random variable of interest h
 as a regularly varying distribution\, we provide su cient conditions for t
 he asymptotic relative error of importance sampling estimators of risk mea
 sures\, such as Value-at-Risk and expected shortfall\, to be small. The re
 sults are illustrated by some numerical examples.\n
LOCATION:Seminar Room 1\, Newton Institute
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