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

Luc Bauwens and Yongdeng Xu
Additional contact information
Luc Bauwens: Université catholique de Louvain, LIDAM/CORE, Belgium

No 3345, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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)
Pages: 18
Date: 2025-01-01
Note: In: International Journal of Forecasting, 2023, vol. 39(2), p. 938-955
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3345

DOI: 10.1016/j.ijforecast.2022.03.005

Access Statistics for this paper

More papers in LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) Voie du Roman Pays 34, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Alain GILLIS ().