Conditional Monte Carlo estimation of quantile sensitives / Michael C. Fu in Management science, Vol. 55 N° 12 (Décembre 2009)

Public ISBD [article]  inManagement science > Vol. 55 N° 12 (Décembre 2009) . - pp. 2019-2027 Titre : Conditional Monte Carlo estimation of quantile sensitives Type de document : texte imprimé Auteurs : Michael C. Fu, Auteur ; L. Jeff Hong, Auteur ; Jian-Qiang Hu, Auteur Article en page(s) : pp. 2019-2027 Note générale : Gestion Langues : Anglais (eng) Mots-clés : Quantiles Value at risk Credit Monte Carlo simulation Gradient estimation Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : Estimating quantile sensitivites is important in many optimizations, 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. DEWEY : 658 ISSN : 0025-1909 [article] Conditional Monte Carlo estimation of quantile sensitives [texte imprimé] / Michael C. Fu, Auteur ; L. Jeff Hong, Auteur ; Jian-Qiang Hu, Auteur . - pp. 2019-2027. Gestion Langues : Anglais (eng) in Management science > Vol. 55 N° 12 (Décembre 2009) . - pp. 2019-2027 Mots-clés : Quantiles Value at risk Credit Monte Carlo simulation Gradient estimation Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : Estimating quantile sensitivites is important in many optimizations, 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. DEWEY : 658 ISSN : 0025-1909

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