Conditional monte carlo estimation of quantile sensitivities

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

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2019-2027
Journal / PublicationManagement Science
Volume55
Issue number12
StatePublished - Dec 2009
Externally publishedYes

Abstract

Estimating quantile sensitivities is important in many optimization applications, from hedging in financial engineering to service-level constraints in inventory control to more general chance constraints in stochastic programming. Recently, Hong (Hong, L. J. 2009. Estimating quantile sensitivities. Oper. Res. 57 118-130) derived a batched infinitesimal perturbation analysis estimator for quantile sensitivities, and Liu and Hong (Liu, G., L. J. Hong. 2009. Kernel estimation of quantile sensitivities. Naval Res. Logist. 56 511-525) derived a kernel estimator. Both of these estimators are consistent with convergence rates bounded by n-1/3 and n -2/5, respectively. In this paper, we use conditional Monte Carlo to derive a consistent quantile sensitivity estimator that improves upon these convergence rates and requires no batching or binning. We illustrate the new estimator using a simple but realistic portfolio credit risk example, for which the previous work is inapplicable. ©2009 INFORMS.

Research Area(s)

  • Credit risk, Gradient estimation, Monte Carlo simulation, Quantiles, Value at risk

Citation Format(s)

Conditional monte carlo estimation of quantile sensitivities. / Fu, Michael C.; Hong, L. Jeff; Hu, Jian-Qiang.

In: Management Science, Vol. 55, No. 12, 12.2009, p. 2019-2027.

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