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Efficient VaR and CVaR measurement via stochastic kriging. (English) Zbl 1414.91421

Summary: In this paper, stochastic kriging (SK) is considered as a metamodeling tool to capture risk-related properties of complex stochastic system outputs. To better assess the tail behavior of the underlying distribution of a system output, we specifically focus on global prediction of value-at-risk and conditional value-at-risk. Going beyond the standard SK framework intended for metamodeling of mean response surfaces, we rethink the original formulation to allow for the flexibility of utilizing different estimation methods for metamodel construction. The resulting impact on the predictive performance of SK is examined in detail. In parallel with the study by X. Chen et al. [Oper. Res. 61, No. 2, 512–528 (2013; Zbl 1329.62356)], we further consider the situation in which noisy gradient information can be incorporated into SK metamodel construction and prediction. The theoretical results are illustrated by two numerical examples.

MSC:

91G70 Statistical methods; risk measures

Citations:

Zbl 1329.62356

Software:

bootstrap
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Full Text: DOI

References:

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