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Managing extreme risk in some major stock markets: An extreme value approach

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  • Karmakar, Madhusudan
  • Shukla, Girja K.
Abstract
The study investigates the relative performance of Value-at-Risk (VaR) models using daily share price index data from six different countries across Asia, Europe and the United States for a period of 10years from January 01, 2000 to December 31, 2009. The main emphasis of the study has been given to Extreme Value Theory (EVT) and to evaluate how well Conditional EVT model performs in modeling tails of distributions and in estimating and forecasting VaR measures. We have followed McNeil and Frey's (2000) two stage approach called Conditional EVT to estimate dynamic VaR. In stage 1, we model the conditional volatility of each series using an appropriate asymmetric GARCH model which serves to filter the return series such that the asymmetric GARCH residuals are closer to iid than the raw return series. In stage 2, we apply EVT to model the fat tails of the asymmetric GARCH residuals. We have compared the accuracy of Conditional EVT approach to VaR estimation with other competing models. The best performing model is found to be the Conditional EVT for the entire sample. To confirm whether the Conditional EVT would still be the best for a sub-period, we have compared the forecasting accuracy for the sub-sample of bull market. Here too the Conditional EVT maintains its superiority even more precisely. Since the Conditional EVT approach clearly dominates other competing models in terms of VaR forecasting, we would advocate the use of the model when managing tail related market risk in such equity markets.

Suggested Citation

  • Karmakar, Madhusudan & Shukla, Girja K., 2015. "Managing extreme risk in some major stock markets: An extreme value approach," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 1-25.
    • Handle: RePEc:eee:reveco:v:35:y:2015:i:c:p:1-25
    DOI: 10.1016/j.iref.2014.09.001
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    References listed on IDEAS

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    Cited by:

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    2. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    3. Sahamkhadam, Maziar & Stephan, Andreas & Östermark, Ralf, 2018. "Portfolio optimization based on GARCH-EVT-Copula forecasting models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 497-506.
    4. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    5. Emmanuel Afuecheta & Chigozie Utazi & Edmore Ranganai & Chibuzor Nnanatu, 2023. "An Application of Extreme Value Theory for Measuring Financial Risk in BRICS Economies," Annals of Data Science, Springer, vol. 10(2), pages 251-290, April.
    6. Wang, Xinya & Liu, Huifang & Huang, Shupei & Lucey, Brian, 2019. "Identifying the multiscale financial contagion in precious metal markets," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 209-219.
    7. Fang, Sheng & Egan, Paul, 2018. "Measuring contagion effects between crude oil and Chinese stock market sectors," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 31-38.
    8. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    9. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    10. Li, Longqing, 2017. "A Comparative Study of GARCH and EVT Model in Modeling Value-at-Risk," MPRA Paper 85645, University Library of Munich, Germany.
    11. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    12. Maashele Kholofelo Metwane & Daniel Maposa, 2023. "Extreme Value Theory Modelling of the Behaviour of Johannesburg Stock Exchange Financial Market Data," IJFS, MDPI, vol. 11(4), pages 1-27, November.
    13. Ra l de Jes s-Guti rrez & Roberto J. Santill n-Salgado, 2019. "Conditional Extreme Values Theory and Tail-related Risk Measures: Evidence from Latin American Stock Markets," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 127-141.

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    More about this item

    Keywords

    Extreme Value Theory; Peak over threshold method; Conditional EVT; Value-at-Risk;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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