An optimization process in Value-at-Risk estimation

Yi-Hou Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish
Pages (from-to)109-116
Number of pages8
JournalReview of Financial Economics
Issue number3
StatePublished - 1 Aug 2010


  • Back-testing
  • Monte Carlo simulation
  • Optimization
  • Value-at-Risk

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