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

Luc Bauwens and Yongdeng Xu

No 2019025, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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 provideid. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecoaosts 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.

Keywords: dynamic conditional correlations; forecasting; multivariate HEAVY; multivariate GARCH; realized correlations (search for similar items in EconPapers)
JEL-codes: C32 C58 G17 (search for similar items in EconPapers)
Date: 2019-12-17
New Economics Papers: this item is included in nep-ecm
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