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Incorporating overnight and intraday returns into multivariate GARCH volatility models

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  • Dhaene, Geert
  • Wu, Jianbin

Abstract

We propose and evaluate mixed-frequency multivariate GARCH models for forecasting low-frequency (weekly) volatility based on high-frequency intraday returns (at 5-minute intervals) and on the overnight returns. The low-frequency conditional volatility matrix is modeled as a weighted sum of an intraday and an overnight component. The components are specified as multivariate GARCH processes of the BEKK type, adapted to the mixed-frequency data setting, and may enter the model as two separate components or as a single one. The models may further be extended by a nonparametrically estimated slowly-varying long-run volatility matrix. We evaluate the models in and out of sample using the 5-minute and overnight returns on four DJIA stocks (AXP, GE, HD, and IBM) from January 1988 to November 2014 and find that they systematically dominate a variety of models that only use lower-frequency data (weekly, daily, or close-to-open and open-to-close returns).

Suggested Citation

  • Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
  • Handle: RePEc:eee:econom:v:217:y:2020:i:2:p:471-495
    DOI: 10.1016/j.jeconom.2019.12.013
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    More about this item

    Keywords

    Mixed-frequency sampling; Overnight returns; Intraday returns; Multivariate GARCH;
    All these keywords.

    JEL classification:

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

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