Incorporating overnight and intraday returns into multivariate GARCH volatility models.

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Authors: Geert Dhaene and Jianbin Wu
Date: Aug. 2020
From: Journal of Econometrics(Vol. 217, Issue 2)
Publisher: Elsevier Science Publishers
Document Type: Report; Brief article
Length: 182 words

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Abstract :

Keywords Mixed-frequency sampling; Overnight returns; Intraday returns; Multivariate GARCH 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). Author Affiliation: (a) KU Leuven, Faculty of Economics and Business, Belgium (b) Nanjing University, School of Economics, China * Corresponding author. Byline: Geert Dhaene [] (a), Jianbin Wu [] (b,*)

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Gale Document Number: GALE|A626116955