Efficient risk estimation using extreme value theory and simulation metamodeling
Pages 385 - 396
Abstract
This paper considers a new approach for constructing metamodels for capturing tail behavior in stochastic systems, e.g., simulation outputs. Specifically, we are concerned with the problem of global estimation of the conditional value-at-risk (CVaR) surface, given (stochastic) responses from a collection of design points. The approach combines stochastic kriging, which has previously been shown to work well for metamodeling of discrete-event simulation output, with extreme value theory, which is a powerful statistical tool for estimating tail behavior. We present the general methodology and promising results of preliminary computational experiments.
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Published In
December 2020
3329 pages
ISBN:9781728194998
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- SCS: Society for Computer Simulation
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IEEE Press
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Published: 13 May 2021
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