Conditional Monte Carlo for sums, with applications to insurance and finance
2018 ◽
Vol 12
(2)
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pp. 455-478
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Keyword(s):
At Risk
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AbstractConditional Monte Carlo replaces a naive estimateZof a numberzby its conditional expectation given a suitable piece of information. It always reduces variance and its traditional applications are in that vein. We survey here other potential uses such as density estimation and calculations for Value-at-Risk and/or expected shortfall, going in part into the implementation in various copula structures. Also the interplay between these different aspects comes into play.