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Multivariate High-Frequency-Based Volatility (HEAVY) Models

Author

Listed:
  • Diaa Noureldin

    (Dept of Economics and Oxford-Man Institute of Quantitative Finance, University of Oxford)

  • Neil Shephard

    (Dept of Economics and Oxford-Man Institute of Quantitative Finance, University of Oxford.)

  • Kevin Sheppard

    (Dept of Economics and Oxford-Man Institute of Quantitative Finance, University of Oxford.)

Abstract

This paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models dynamics and highlight their di¤erences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are ob- tained for both forecast variances and correlations.

Suggested Citation

  • Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:1101
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    References listed on IDEAS

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    More about this item

    Keywords

    HEAVY model; GARCH; multivariate volatility; realized covariance; covariance targeting; multi-step forecasting; Wishart distribution.;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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