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Relative Efficiency of Component GARCH-EVT Approach in Managing Intraday Market Risk

Author

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  • Samit Paul

    (Indian Institute of Management Calcutta, India)

  • Madhusudan Karmakar

    (Indian Institute of Management Lucknow, India)

Abstract

The purpose of this study is to estimate intraday Value-at-Risk (VaR) and Expected Shortfall (ES) of high frequency stock price indices taken from select markets of the world. The stylized properties indicate that the return series exhibit skewed and leptokurtic distributions, volatility clustering, periodicity of volatility and long memory process in volatility, all of which together suggest the usage of Component GARCH- EVT combined approach on periodicity adjusted return series to forecast accurate intraday VaR and ES. Hence we estimate intraday VaR and ES using Component GARCH-EVT combined approach with different innovation distributions such as normal, student-t and skewed student-t and compare its relative accuracy with the benchmark GARCH-EVT model with different distributions. The Component GARCH-EVT models in general perform better than GARCH-EVT models and the model with skewed student-t innovations forecasts more accurately. The study is useful for market participants involved in frequent intraday trading in such markets.

Suggested Citation

  • Samit Paul & Madhusudan Karmakar, 2017. "Relative Efficiency of Component GARCH-EVT Approach in Managing Intraday Market Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 21(4), pages 247-283, December.
    • Handle: RePEc:mfj:journl:v:21:y:2017:i:4:p:247-283
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    References listed on IDEAS

    as
    1. 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.
    2. Tauchen, George, 2001. "Notes on financial econometrics," Journal of Econometrics, Elsevier, vol. 100(1), pages 57-64, January.
    3. Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006. "Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 29-58.
    4. Christoffersen, Peter, 2011. "Elements of Financial Risk Management," Elsevier Monographs, Elsevier, edition 2, number 9780123744487.
    5. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    6. Bystrom, Hans N. E., 2004. "Managing extreme risks in tranquil and volatile markets using conditional extreme value theory," International Review of Financial Analysis, Elsevier, vol. 13(2), pages 133-152.
    7. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836, Decembrie.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Ergün, A. Tolga & Jun, Jongbyung, 2010. "Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 264-272, August.
    10. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    11. Cotter, John, 2007. "Varying the VaR for unconditional and conditional environments," Journal of International Money and Finance, Elsevier, vol. 26(8), pages 1338-1354, December.
    12. Fernandez, Viviana, 2005. "Risk management under extreme events," International Review of Financial Analysis, Elsevier, vol. 14(2), pages 113-148.
    13. 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.
    14. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
    15. Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
    16. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December.
    17. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    18. Bali, Turan G. & Neftci, Salih N., 2003. "Disturbing extremal behavior of spot rate dynamics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 455-477, September.
    19. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    20. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    21. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    22. Satheesh Aradhyula & A. Tolga Ergun, 2004. "Trading collar, intraday periodicity and stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 909-913.
    23. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    24. Toshiaki Watanabe, 2012. "Quantile Forecasts Of Financial Returns Using Realized Garch Models," The Japanese Economic Review, Japanese Economic Association, vol. 63(1), pages 68-80, March.
    25. Martin Martens & Yuan‐Chen Chang & Stephen J. Taylor, 2002. "A Comparison of Seasonal Adjustment Methods When Forecasting Intraday Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 25(2), pages 283-299, June.
    26. Chavez-Demoulin, V. & McGill, J.A., 2012. "High-frequency financial data modeling using Hawkes processes," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3415-3426.
    27. Ahmed Ghorbel & Abdelwahed Trabelsi, 2008. "Predictive performance of conditional Extreme Value Theory in Value-at-Risk estimation," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 1(2), pages 121-148.
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    More about this item

    Keywords

    deseasonalized; intraday; value at risk; expected shortfall; component GARCH; EVT;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G19 - Financial Economics - - General Financial Markets - - - Other

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