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

Luc Bauwens and Yongdeng Xu

International Journal of Forecasting, 2023, vol. 39, issue 2, 938-955

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.

Keywords: Correlation forecasting; Dynamic conditional correlation; Equicorrelation; High-frequency data; Multivariate volatility (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Working Paper: DCC and DECO-HEAVY: a multivariate GARCH model based on realized variances and correlations (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:938-955

DOI: 10.1016/j.ijforecast.2022.03.005

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