Asymptotically Valid Confidence Intervals for Quantiles and Values-at-Risk When Applying Latin Hypercube Sampling
نویسنده
چکیده
Quantiles, which are also known as values-at-risk in finance, are often used as risk measures. Latin hypercube sampling (LHS) is a variance-reduction technique (VRT) that induces correlation among the generated samples in such a way as to increase efficiency under certain conditions; it can be thought of as an extension of stratified sampling in multiple dimensions. This paper develops asymptotically valid confidence intervals for quantiles that are estimated via simulation using LHS. Keywords-quantile; value-at-risk; Latin hypercube sampling; variance reduction; confidence interval.
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Variance - Reduction Techniques for Estimating Quantiles and Value - at - Risk
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