@inproceedings{90ff325d24ec45aaa405f651ec715d54,
title = "Bias reduction in estimating quantile sensitivities",
abstract = "In this paper, we introduce an Infinitesimal Perturbation Analysis (IPA) estimator with jackknifing to estimate quantile sensitivities, and theoretically prove the two-fold jackknife method reduces bias by eliminating the order 1/n bias in the original IPA estimator. Numerical examples in finance on portfolio return and options pricing are presented to illustrate the superiority of the new estimator over the original IPA estimator, especially for high and low quantile levels. Antithetic variates are used to further reduce the variance.",
keywords = "Discrete event modeling and simulation, Monte Carlo simulation, Perturbation analysis, Sensitivity analysis, Stochastic hybrid systems",
author = "Guangxin Jiang and Fu, \{Michael C.\} and Chenglong Xu",
note = "Publisher Copyright: {\textcopyright} IFAC.; 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 ; Conference date: 24-08-2014 Through 29-08-2014",
year = "2014",
doi = "10.3182/20140824-6-za-1003.02029",
language = "英语",
series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
publisher = "IFAC Secretariat",
pages = "10463--10468",
editor = "Edward Boje and Xiaohua Xia",
booktitle = "19th IFAC World Congress IFAC 2014, Proceedings",
}