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High Frequency Multiplicative Component Garch


  • Magdalena E. Sokalska
  • Ananda Chanda

    () (Finance New York University)

  • Robert F. Engle


This paper proposes a new way of modeling and forecasting intraday returns. We decompose the volatility of high frequency asset returns into components that may be easily interpreted and estimated. The conditional variance is expressed as a product of daily, diurnal and stochastic intraday volatility components. This model is applied to a comprehensive sample consisting of 10-minute returns on more than 2500 US equities. We apply a number of different specifications. Apart from building a new model, we obtain several interesting forecasting results. In particular, it turns out that forecasts obtained from the pooled cross section of groups of companies seem to outperform the corresponding forecasts from company-by-company estimation.

Suggested Citation

  • Magdalena E. Sokalska & Ananda Chanda & Robert F. Engle, 2005. "High Frequency Multiplicative Component Garch," Computing in Economics and Finance 2005 409, Society for Computational Economics.
  • Handle: RePEc:sce:scecf5:409

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    References listed on IDEAS

    1. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    2. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    3. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December.
    4. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    5. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    6. Torben G. Andersen & Tim Bollerslev, 1996. "DM-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies," NBER Working Papers 5783, National Bureau of Economic Research, Inc.
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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.
    2. Dötz, Niko & Fischer, Christoph, 2010. "What can EMU countries' sovereign bond spreads tell us about market perceptions of default probabilities during the recent financial crisis?," Discussion Paper Series 1: Economic Studies 2010,11, Deutsche Bundesbank.
    3. Chen Xilong & Ghysels Eric & Wang Fangfang, 2011. "HYBRID GARCH Models and Intra-Daily Return Periodicity," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-28, February.
    4. Pierre Giot & Joachim Grammig, 2006. "How large is liquidity risk in an automated auction market?," Empirical Economics, Springer, vol. 30(4), pages 867-887, January.

    More about this item


    ARCH; Intra-day Returns; Volatility;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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