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Market risk models for intraday data

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  • GIOT, Pierre

Abstract

In this paper, market risk at an intraday time horizon is quantified using normal GARCH, Student GARCH, RiskMetrics and high-frequency duration (log-ACD) models set in the framework of the conditional VaR methodology. Because of the small time horizon of the intraday returns (15 and 30 minute returns in this paper), an evaluation of intraday market risk 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 the market risk models. The four models are applied to intraday returns data for three stocks traded on the New York Stock Exchange and it is shown that the Student GARCH model performs best. The use of price durations as a measure of risk on time is commented upon.
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Suggested Citation

  • GIOT, Pierre, 2005. "Market risk models for intraday data," LIDAM Reprints CORE 1850, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:1850
    DOI: 10.1080/1351847032000143396
    Note: In : The European Journal of Finance, 11(4), 309-324, 2005
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    1. 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.
    2. J.L. Prigent & O. Renault & O. Scaillet., 1999. "An autoregressive conditional binomial option pricing model under stochastic rates," THEMA Working Papers 99-40, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    3. Olivier V. Pictet & Michel M. Dacorogna & Ulrich A. Muller, 1996. "Heavy tails in high-frequency financial data," Working Papers 1996-12-11, Olsen and Associates.
    4. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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