Dynamic Factor Multivariate GARCH Model

Forthcoming, Computational Statistics and Data Analysis

27 Pages Posted: 17 Jul 2012 Last revised: 9 Oct 2012

See all articles by Andre A. P. Santos

Andre A. P. Santos

CUNEF Universidad

Guilherme V. Moura

Federal University of Santa Catarina (UFSC) - Department of Economics

Date Written: June 24, 2012

Abstract

Factor models are well established as promising alternatives to obtain covariance matrices of portfolios containing a very large number of assets. In this paper, we consider a novel multivariate factor GARCH speci cation with a flexible modeling strategy for the common factors, for the individual assets, and for the factor loads. We apply the proposed model to obtain minimum variance portfolios of all stocks that belonged to the S&P100 during the sample period and show that it delivers less risky portfolios in comparison to benchmark models, including existing factor approaches.

Keywords: dynamic conditional correlation (DCC), forecasting, Kalman filter, learning, CAPM, performance evaluation, Sharpe ratio

Suggested Citation

A. P. Santos, Andre and Moura, Guilherme Valle, Dynamic Factor Multivariate GARCH Model (June 24, 2012). Forthcoming, Computational Statistics and Data Analysis , Available at SSRN: https://ssrn.com/abstract=2110298 or http://dx.doi.org/10.2139/ssrn.2110298

Andre A. P. Santos (Contact Author)

CUNEF Universidad ( email )

Calle de los Pirineos 55
Madrid, 28040
Spain

Guilherme Valle Moura

Federal University of Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

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