Estimating quantile sensitivities

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

51 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)118-130
Journal / PublicationOperations Research
Volume57
Issue number1
Publication statusPublished - Jan 2009
Externally publishedYes

Abstract

Quantiles of a random performance serve as important alternatives to the usual expected value. They are used in the financial industry as measures of risk and in the service industry as measures of service quality. To manage the quantile of a performance, we need to know how changes in the input parameters affect the output quantiles, which are called quantile sensitivities. In this paper, we show that the quantile sensitivities can be written in the form of conditional expectations. Based on the conditional-expectation form, we first propose an infinitesimal-perturbation-analysis (IPA) estimator. The IPA estimator is asymptotically unbiased, but it is not consistent. We then obtain a consistent estimator by dividing data into batches and averaging the IPA estimates of all batches. The estimator satisfies a central limit theorem for the i.i.d. data, and the rate of convergence is strictly slower than n- 1/3. The numerical results show that the estimator works well for practical problems. © 2009 INFORMS.

Research Area(s)

  • Quantile, Sensitivity analysis, Simulation, Statistical analysis, Value-at-risk

Citation Format(s)

Estimating quantile sensitivities. / Hong, L. Jeff.

In: Operations Research, Vol. 57, No. 1, 01.2009, p. 118-130.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review