Stock index Value-at-Risk forecasting: A realized volatility extreme value theory approach
AbstractIn this study, we propose the use of Heterogeneous Autoregressive (HAR) type realized volatility models in combination with the Extreme Value Theory (EVT) method for Value-at-Risk (VaR) forecasting. The proposed model accounts for the long memory property of the realized volatility and the fat tails of the returns distribution. The out-of-sample forecasting results, based on the S&P 500 stock index, indicate that the HAR-type-EVT models outperform their GARCH-type counterparts in terms of statistical and regulatory accuracy as well as capital efficiency. The HAR-GARCH-EVT model, which also accounts for the conditional heteroscedasticity of the HAR errors, is the overall best performing model as it generates accurate VaR estimates that minimize the Basel II regulatory capital during both the full out-of-sample period and the 2007-2009 crisis period.
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Bibliographic InfoArticle provided by AccessEcon in its journal Economics Bulletin.
Volume (Year): 32 (2012)
Issue (Month): 1 ()
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Value-at-Risk; High frequency data; Extreme value Theory; Financial Crisis; GARCH;
Find related papers by JEL classification:
- G2 - Financial Economics - - Financial Institutions and Services
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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- Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2008.
"Quantile forecasts of daily exchange rate returns from forecasts of realized volatility,"
Journal of Empirical Finance,
Elsevier, vol. 15(4), pages 729-750, September.
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- Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
- Dimitrios P. Louzis & Spyros Xanthopoulos-Sisinis & Apostolos P. Refenes, 2012. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Applied Economics, Taylor and Francis Journals, vol. 44(27), pages 3533-3550, September.
- Angelidis, Timotheos & Degiannakis, Stavros, 2008. "Volatility forecasting: Intra-day versus inter-day models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 449-465, December.
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