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A Coupling of Extreme-Value Theory and Volatility Updating with Value-at-Risk Estimation in Emerging Markets: A South African Test

Author

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  • Anthony J. Seymour

    (University of Cape Town, South Africa)

  • Daniel A. Polakow

    (University of Cape Town and Cadiz Holdings, South Africa)

Abstract

This research is aimed at a formal appraisal of recent advancements in stochastic volatility modeling and extreme-value theory to application of value-at-risk computation in particularly volatile markets. Established methods such as historical simulation are prone to underestimating value-at-risk in such developing markets. Two contemporary methods of value-at-risk calculation are tested on a representative portfolio of South African stocks. The first method incorporates extreme value theory. The second model includes both extreme value theory and volatility updating (via GARCH-type modeling). The combined GARCH-type time-series approach and extreme value theory model is found to provide significantly better results than both straightforward historical simulation as well as the extreme value model. In no instance, however, were results on these VaR methods as good as those obtained when the same methods were tested in developed markets.This research highlights noteworthy improvements to value-at-risk estimation efficacy in volatile emerging markets, and also stresses the need for further work into the estimation of value-at-risk in this context.

Suggested Citation

  • Anthony J. Seymour & Daniel A. Polakow, 2003. "A Coupling of Extreme-Value Theory and Volatility Updating with Value-at-Risk Estimation in Emerging Markets: A South African Test," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 3-23, March-Jun.
    • Handle: RePEc:mfj:journl:v:7:y:2003:i:1-2:p:3-23
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    References listed on IDEAS

    as
    1. Jon Danielsson & Casper G. De Vries, 2000. "Value-at-Risk and Extreme Returns," Annals of Economics and Statistics, GENES, issue 60, pages 239-270.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Paul Embrechts & Sidney Resnick & Gennady Samorodnitsky, 1999. "Extreme Value Theory as a Risk Management Tool," North American Actuarial Journal, Taylor & Francis Journals, vol. 3(2), pages 30-41.
    4. repec:adr:anecst:y:2000:i:60:p:10 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Timotheos Angelidis & Alexandros Benos, 2008. "Value-at-Risk for Greek Stocks," Multinational Finance Journal, Multinational Finance Journal, vol. 12(1-2), pages 67-104, March-Jun.
    2. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
    3. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    4. L. K. Hotta & E. C. Lucas & H. P Palaro, 2008. "Estimation of VaR Using Copula and Extreme Value Theory," Multinational Finance Journal, Multinational Finance Journal, vol. 12(3-4), pages 205-218, September.
    5. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.
    6. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    7. Goran Andjelic & Ivana Milosev & Vladimir Djakovic, 2010. "Extreme Value Theory In Emerging Markets," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 55(185), pages 63-106, April - J.

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    More about this item

    Keywords

    backtesting; extreme value theory; GARCH; historical simulation; RiskMetrics; value-at-risk;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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