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Variance Reduction Techniques for Monte Carlo Estimates of Value at Risk
Company: SimCorp
Company Url: Click here to open
Year Of Publication: 2000
Month Of Publication: February
Pages: 17
Download Count: 1547
View Count: 10044
Comment Num: 0
Language: EN
Source: white paper
Who Can Read: Free
Date: 9-19-2002
Publisher: Administrator
Summary
The sampling variance of Monte Carlo estimates of Value at Risk is reduced using Importance Sampling and Control Variates. Both Importance Sampling and Control Variates are based on second-order approximations of the change in portfolio value, and the variance reduction technique yields very high variance reductions when this approximation is reasonable. This involves portfolios where Delta's are large, relative to higher order derivatives. The methods improve the variance reduction relative to the Importance Sampling and Stratified Sampling approach suggested by [Glasserman et al., 1999a].
Author(s)
Fuglesbjerg, Brian Sign in to follow this author
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