Moderate deviation principles for importance sampling estimators of risk measures
| Autor: | Pierre Nyquist |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Deviation risk measure 021103 operations research General Mathematics Risk measure 0211 other engineering and technologies Estimator 02 engineering and technology 01 natural sciences 010104 statistics & probability Expected shortfall Statistics Econometrics Large deviations theory 0101 mathematics Statistics Probability and Uncertainty Value at risk Importance sampling Quantile Mathematics |
| Zdroj: | Journal of Applied Probability. 54:490-506 |
| ISSN: | 1475-6072 0021-9002 |
| DOI: | 10.1017/jpr.2017.13 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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