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Modelling Realized Covariances and Returns

Author

Listed:
  • Xin Jin

    () (Department of Economics, University of Toronto, Canada)

  • John M. Maheu

    () (Department of Economics, University of Toronto, Canada; The Rimini Centre for Economic Analysis (RCEA), Italy)

Abstract

This paper proposes new dynamic component models of returns and realized covariance (RCOV) matrices based on time-varying Wishart distributions. Bayesian estimation and model comparison is conducted with a range of multivariate GARCH models and existing RCOV models from the literature. The main method of model comparison consists of a term-structure of density forecasts of returns for multiple forecast horizons. The new joint return-RCOV models provide superior density forecasts for returns from forecast horizons of 1 day to 3 months ahead as well as improved point forecasts for realized covariances. Global minimum variance portfolio selection is improved for forecast horizons up to 3 weeks out.

Suggested Citation

  • Xin Jin & John M. Maheu, 2011. "Modelling Realized Covariances and Returns," Working Paper series 08_11, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:08_11
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    Keywords

    Wishart distribution; predictive likelihoods; density forecasts; MCMC;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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