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

Luc Bauwens and Yongdeng Xu

No E2019/5, Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section

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 new HEAVY models outperform the BEKKHEAVY model based on realized covariances, and the BEKK, DCC and DECO multivariate GARCH models based exclusively on daily data.

Keywords: correlation forecasting; dynamic conditional correlation; equicorrelation; high-frequency data; multivariate volatility. (search for similar items in EconPapers)
JEL-codes: C32 C58 G17 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2019-02, Revised 2021-08
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Related works:
Journal Article: DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:cdf:wpaper:2019/5

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