Moderate deviation principles for importance sampling estimators of risk measures

P. Nyquist

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Samenvatting

Importance sampling has become an important tool for the computation of extreme quantiles and tail-based risk measures. For estimation of such nonlinear functionals of the underlying distribution, the standard efficiency analysis is not necessarily applicable. In this paper we therefore study importance sampling algorithms by considering moderate deviations of the associated weighted empirical processes. Using a delta method for large deviations, combined with classical large deviation techniques, the moderate deviation principle is obtained for importance sampling estimators of two of the most common risk measures: value at risk and expected shortfall.

Originele taal-2Engels
Pagina's (van-tot)490-506
Aantal pagina's17
TijdschriftJournal of Applied Probability
Volume54
Nummer van het tijdschrift2
DOI's
StatusGepubliceerd - 1 jun 2017
Extern gepubliceerdJa

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