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Stock index Value-at-Risk forecasting: A realized volatility extreme value theory approach

Author

Listed:
  • Dimitrios P. Louzis

    (Bank of Greece / Athens University of Economics and Business)

  • Spyros Xanthopoulos - Sissinis

    (Athens University of Economics and Business)

  • Apostolos P. Refenes

    (Athens University of Economics and Business)

Abstract

In 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.

Suggested Citation

  • Dimitrios P. Louzis & Spyros Xanthopoulos - Sissinis & Apostolos P. Refenes, 2012. "Stock index Value-at-Risk forecasting: A realized volatility extreme value theory approach," Economics Bulletin, AccessEcon, vol. 32(1), pages 981-991.
    • Handle: RePEc:ebl:ecbull:eb-11-00870
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Value-at-Risk; High frequency data; Extreme value Theory; Financial Crisis; GARCH;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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