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A Generalized Extreme Value Approach to Financial Risk Measurement

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  • TURAN G. BALI

Abstract

This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in‐sample and out‐of‐sample performance results indicate that the Box–Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions.

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  • Turan G. Bali, 2007. "A Generalized Extreme Value Approach to Financial Risk Measurement," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1613-1649, October.
    • Handle: RePEc:wly:jmoncb:v:39:y:2007:i:7:p:1613-1649
    DOI: 10.1111/j.1538-4616.2007.00081.x
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    3. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
    4. Madhusudan Karmakar, 2013. "Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach," Review of Financial Economics, John Wiley & Sons, vol. 22(3), pages 79-85, September.
    5. Rizwan, Muhammad Suhail & Ahmad, Ghufran & Ashraf, Dawood, 2020. "Systemic risk: The impact of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    6. Aranit Muja, 2018. "Extreme Value of Intraday Returns," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 7, November.
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