Forecasting multivariate volatility using the VARFIMA model on realized covariance cholesky factors

Roxana Halbleib () and Valeri Voev ()

ULB Institutional Repository from ULB -- Universite Libre de Bruxelles

Abstract: This 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. By modelling the Cholesky factors of the covariance matrices, 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.

Keywords: Forecasting; Fractional integration; Portfolio optimization; Realized covariance; Stochastic dominance (search for similar items in EconPapers)
Date: 2011-02
Note: SCOPUS: ar.j
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Citations: View citations in EconPapers (6)

Published in: Jahrbücher für Nationalökonomie und Statistik (2011) v.231 n° 1,p.134-152

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Related works:
Journal Article: Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors (2011)
Working Paper: Forecasting Multivariate Volatility Using the VARFIMA Model on Realized Covariance Cholesky Factors (2010)
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