Modelling Realized Covariances and Returns
AbstractThis paper proposes new dynamic component models of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications are linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast. Realized covariance matrices are constructed for 5 stocks using high-frequency intraday prices based on positive semi-definite realized kernel estimates. The models are compared based on a term-structure of density forecasts of returns for multiple forecast horizons. Relative to multivariate GARCH models that use only daily returns, the joint RCOV and return models provide significant improvements in density forecasts from forecast horizons of 1 day to 3 months ahead. Global minimum variance portfolio selection is improved for forecast horizons up to 3 weeks out.
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Bibliographic InfoPaper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-408.
Length: 33 pages
Date of creation: 16 Jul 2010
Date of revision:
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eigenvalues; dynamic conditional correlation; predictive likelihoods; MCMC;
Other versions of this item:
- 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-07-24 (All new papers)
- NEP-ECM-2010-07-24 (Econometrics)
- NEP-ETS-2010-07-24 (Econometric Time Series)
- NEP-FOR-2010-07-24 (Forecasting)
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