DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations

Bauwens, Luc;Xu, Yongdeng
(2019) , 54 pages

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  • Bauwens, Lucorcid-logoUCLouvain
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  • Xu, YongdengCardiff University
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Abstract
This paper introduces the DCC-HEAVY and DECO-HEAVY models, which are dynamic models for conditional variances and correlations for 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 new HEAVY models outperform the BEKK-HEAVY model based on realized covariances, and the BEKK, DCC and DECO multivariate GARCH models based exclusively on daily data.
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Bauwens, L., & Xu, Y. (2019). DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations (CORE Discussion Papers 2019/25). https://hdl.handle.net/2078.5/123534