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

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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 correla- tions are provided. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecasts 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.

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  • Bauwens, Luc & Xu, Yongdeng, 2019. "DCC-HEAVY: a multivariate GARCH model based on realized variances and correlations," Cardiff Economics Working Papers E2019/5, Cardiff University, Cardiff Business School, Economics Section.
  • Handle: RePEc:cdf:wpaper:2019/5
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    1. Neil Shephard & Kevin Sheppard, 2010. "Realising the future: forecasting with high-frequency-based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 197-231.
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    Keywords

    Dynamic conditional correlations; Forecasting; Multivariate HEAVY; Multivariate GARCH; Realized correlations.;

    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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