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An optimization process in Value‐at‐Risk estimation

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  • Alex YiHou Huang

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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  • Alex YiHou Huang, 2010. "An optimization process in Value‐at‐Risk estimation," Review of Financial Economics, John Wiley & Sons, vol. 19(3), pages 109-116, August.
    • Handle: RePEc:wly:revfec:v:19:y:2010:i:3:p:109-116
    DOI: 10.1016/j.rfe.2010.03.001
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    1. Saeed Shaker-Akhtekhane & Solmaz Poorabbas, 2023. "Value-at-Risk Estimation Using an Interpolated Distribution of Financial Returns Series," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(1), pages 1-6.

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