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Dynamic Factor Multivariate GARCH Model

Forthcoming, Computational Statistics and Data Analysis

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

Andre A. P. Santos

Universidade Federal de Santa Catarina (UFSC) - Department of Economics

Guilherme V. Moura

Universidade Federal de 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 V., 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)

Universidade Federal de Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

HOME PAGE: http://sites.google.com/site/andreportela

Guilherme Valle Moura

Universidade Federal de Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

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