DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations

Bauwens, Luc and Xu, Yongdeng ORCID: https://orcid.org/0000-0001-8275-1585 2023. DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations. International Journal of Forecasting 39 (2) , pp. 938-955. 10.1016/j.ijforecast.2022.03.005

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Abstract

This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances and correlations of daily returns based on measures of realized variances and correlations built from intraday data. Formulas for multi-step forecasts of conditional variances and correlations are provided. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecasts the scalar HEAVY models outperform the scalar BEKK-HEAVY model based on realized covariances and the scalar BEKK, DCC, and DECO multivariate GARCH models based exclusively on daily data.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier
ISSN:	0169-2070
Date of First Compliant Deposit:	24 June 2022
Date of Acceptance:	4 May 2022
Last Modified:	03 May 2023 17:11
URI:	https://orca.cardiff.ac.uk/id/eprint/150752

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