| Titre : | Conditional Monte Carlo estimation of quantile sensitives |
| Auteurs : | Michael C. Fu, Auteur ; L. Jeff Hong, Auteur ; Jian-Qiang Hu, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Management science (Vol. 55 N° 12, Décembre 2009) |
| Article en page(s) : | pp. 2019-2027 |
| Note générale : | Gestion |
| Langues : | Anglais |
| Index. décimale : | 658 (Organisation des entreprises. Techniques du commerce) |
| Tags : | Quantiles Value at risk Credit Monte Carlo simulation Gradient estimation |
| 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 |
