An optimization process in Value-at-Risk estimation

Alex YiHou Huang

Review of Financial Economics, 2010, vol. 19, issue 3, pages 109-116

Abstract: A new method is proposed to estimate Value-at-Risk (VaR) by Monte Carlo simulation with optimal back-testing results. The Monte Carlo simulation is adjusted through an iterative process to accommodate recent shocks, thereby taking into account the latest market conditions. Empirical validation covering the current financial crisis shows that VaR estimation via the optimization process is relatively reliable and consistent, and generally outperforms the VaR generated by a simple Monte Carlo simulation. This is particularly true in cases when the out-of-sample evaluation sample spans a lengthy period, as the traditional method tends to underestimate the number of extreme shocks.

Keywords: Value-at-Risk; Optimization; Back-testing; Monte; Carlo; simulation (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6W61 ... 23a298e524c3466d769b
Full text for ScienceDirect subscribers only

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: http://EconPapers.repec.org/RePEc:eee:revfin:v:19:y:2010:i:3:p:109-116

Access Statistics for this article

Review of Financial Economics is edited by T. K. Mukherjee and G. Whitney

More articles in Review of Financial Economics from Elsevier
Series data maintained by Wendy Shamier ().