Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors
AbstractThis paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariancematrices, the model generates positive definite, but biased covariance forecasts. In this paper, we provide empirical evidence that parsimonious versions of the model generate the best covariance forecasts in the absence of bias correction. Moreover, we show by means of stochastic dominance tests that any risk averse investor, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches.
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Bibliographic InfoArticle provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.
Volume (Year): 231 (2011)
Issue (Month): 1 (February)
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Forecasting; fractional integration; stochastic dominance; portfolio optimization; realized covariance;
Other versions of this item:
- Roxana Halbleib & Valerie Voev, 2010. "Forecasting Multivariate Volatility Using the VARFIMA Model on Realized Covariance Cholesky Factors," Working Papers ECARES ECARES 2010-041, ULB -- Universite Libre de Bruxelles.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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