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

Author

Listed:
  • BAUWENS Luc,

    (Université catholique de Louvain, CORE, Belgium)

  • XU Yongdeng,

    (Cardiff University)

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.

Suggested Citation

  • BAUWENS Luc, & XU Yongdeng,, 2019. "DCC-HEAVY: A multivariate GARCH model based on realized variances and correlations," LIDAM Discussion Papers CORE 2019025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2019025
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    dynamic conditional correlations; forecasting; multivariate HEAVY; multivariate GARCH; realized correlations;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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