Kernel estimation of quantile sensitivities

Guangwu Liu and Liu Jeff Hong

Naval Research Logistics (NRL), 2009, vol. 56, issue 6, 511-525

Abstract: Quantiles, also known as value‐at‐risks in the financial industry, are important measures of random performances. Quantile sensitivities provide information on how changes in input parameters affect output quantiles. They are very useful in risk management. In this article, we study the estimation of quantile sensitivities using stochastic simulation. We propose a kernel estimator and prove that it is consistent and asymptotically normally distributed for outputs from both terminating and steady‐state simulations. The theoretical analysis and numerical experiments both show that the kernel estimator is more efficient than the batching estimator of Hong 9. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

Downloads: (external link)
https://doi.org/10.1002/nav.20358

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:navres:v:56:y:2009:i:6:p:511-525

Access Statistics for this article

More articles in Naval Research Logistics (NRL) from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().