Publication detail

On the modelling and forecasting multivariate realized volatility: Generalized Heterogeneous Autoregressive (GHAR) model

Author(s): doc. PhDr. Jozef Baruník Ph.D.,
PhDr. František Čech Ph.D.,
Type: IES Working Papers
Year: 2014
Number: 23
ISSN / ISBN:
Published in: IES Working Papers 23/2014
Publishing place: Prague
Keywords: GHAR, portfolio optimisation, economic evaluation
JEL codes: C18, C58, G15
Suggested Citation: Baruník J., Čech F. (2014). “On the modelling and forecasting multivariate realized volatility: Generalized Heterogeneous Autoregressive (GHAR) model” IES Working Paper 23/2014. IES FSV. Charles University.
Grants: GAUK 1198214: Multivariate volatility modeling of medium and large size portfolios
Abstract: We introduce a methodology for dynamic modelling and forecasting of realized covariance matrices based on generalization of the heterogeneous autoregressive model (HAR) for realized volatility. Multivariate extensions of popular HAR framework leave substantial information unmodeled in residuals. We propose to employ a system of seemingly unrelated regressions to capture the information. The newly proposed generalized heterogeneous autoregressive (GHAR) model is tested against natural competing models. In order to show the economic and statistical gains of the GHAR model, portfolio of various sizes is used. We find that our modeling strategy outperforms competing approaches in terms of statistical precision, and provides economic gains in terms of mean-variance trade-o . Additionally, our results provide a comprehensive comparison of the performance when realized covariance and more ecient, noise-robust multivariate realized kernel estimator, is used. We study the contribution of both estimators across di erent sampling frequencies, and we show that the multivariate realized kernel estimator delivers further gains compared to realized covariance estimated on higher frequencies.
Downloadable: wp_2014_23_Barunik_Cech

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