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An optimization process in Value-at-Risk estimation

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  • Huang, Alex YiHou

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.

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  • Huang, Alex YiHou, 2010. "An optimization process in Value-at-Risk estimation," Review of Financial Economics, Elsevier, vol. 19(3), pages 109-116, August.
    • Handle: RePEc:eee:revfin:v:19:y:2010:i:3:p:109-116
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    Cited by:

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    3. Kostas Andriosopoulos & Nikos Nomikos, 2012. "Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach," RSCAS Working Papers 2012/47, European University Institute.

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