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Intraday value-at-risk

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  • GIOT, Pierre

Abstract

In this paper, we apply a collection of parametric (Normal, Normal GARCH, Student GARCH, RiskMetrics and high-frequency duration models) and non-parametric (empirical quantile, extreme distributions models) Value-at-Risk (VaR) techniques to intraday data for three stocks traded on the NewY ork Stock Exchange. Because of the small time horizon of the intraday returns (15 and 30 minute returns), intraday VaR can be useful to market participants (traders, market makers)involved in frequent trading. As expected, the volatility features an important intraday seasonality, which must be removed prior to using theVaR models. The estimation and assessment of the VaR techniques indicate that the data displays a high kurtosis (fat tails), and that VaR models should take this important feature into account. More particularly, Student GARCH, empirical quantile and extreme distributions models perform relatively well.

Suggested Citation

  • GIOT, Pierre, 2000. "Intraday value-at-risk," CORE Discussion Papers 2000045, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2000045
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    References listed on IDEAS

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    3. Dominique M. Guillaume & Olivier V. Pictet & Michel M. Dacorogna, "undated". "On the intra-daily performance of GARCH processes," Working Papers 1994-07-31, Olsen and Associates.
    4. Jean -Luc Prigent & Olivier Renault & Olivier Scaillet, 1999. "An Autoregressive Conditional Binomial Option Pricing Model," Working Papers 99-65, Center for Research in Economics and Statistics.
    5. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    6. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    11. Engle, Robert F. & Russell, Jeffrey R., 1997. "Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 187-212, June.
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    Cited by:

    1. Großmaß Lidan, 2014. "Liquidity and the Value at Risk," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(5), pages 572-602, October.

    More about this item

    Keywords

    Intraday volatility; Intraday Value-at-Risk; Duration models; NYSE.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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