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Forecasting daily volatility with intraday data


  • Bart Frijns
  • Dimitris Margaritis


The aim of this paper is to assess to what extent intraday data can explain and predict end-of-the-day volatility. Using a realized volatility measure as proposed by Andersen, T., T. Bollerslev, F. Diebold, and P. Labys. 2001. The distribution of realized exchange rate volatility. Journal of the American Statistical Association 96: 42-55, we hypothesize that volatility generated at the start of the day is an important predictor of daily volatility either on its own accord or in conjunction with information about the seasonal pattern characterizing intraday volatility. We address the question of how much information needs to arrive to the market before a good predictor can be formed. Using data from a specialist market (NYSE), a dealer market (Nasdaq) and a continuous auction market (Paris Bourse), we investigate how different trading structures may affect intraday volatility formation. As a preview to our results, we find that the explanatory power of first-hour volatility for daily volatility is as high as 68%, whereas the average volatility generated during this first hour is <30%. Comparison to a standard GARCH model shows that the forecasts based on the intraday data are generally highly informative both on their own accord and in combination with the GARCH forecasts.

Suggested Citation

  • Bart Frijns & Dimitris Margaritis, 2008. "Forecasting daily volatility with intraday data," The European Journal of Finance, Taylor & Francis Journals, vol. 14(6), pages 523-540.
  • Handle: RePEc:taf:eurjfi:v:14:y:2008:i:6:p:523-540 DOI: 10.1080/13518470802187644

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    References listed on IDEAS

    1. Zhao, Quanshui, 2001. "Asymptotically Efficient Median Regression In The Presence Of Heteroskedasticity Of Unknown Form," Econometric Theory, Cambridge University Press, vol. 17(04), pages 765-784, August.
    2. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    3. Richard Blundell & James L. Powell, 2001. "Endogeneity in nonparametric and semiparametric regression models," CeMMAP working papers CWP09/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Michelle L. Barnes & Anthony W. Hughes, 2002. "A quantile regression analysis of the cross section of stock market returns," Working Papers 02-2, Federal Reserve Bank of Boston.
    5. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
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    Cited by:

    1. Jan Hanousek & Evžen Kočenda, 2011. "Foreign News and Spillovers in Emerging European Stock Markets," Review of International Economics, Wiley Blackwell, vol. 19(1), pages 170-188, February.
    2. Daniel Jubinski & Amy F. Lipton, 2012. "Equity volatility, bond yields, and yield spreads," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(5), pages 480-503, May.


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