Title: Optimal portfolio allocation strategies with dynamic factor models

Authors: Nikos S. Thomaidis, Efthimios Roumpis, Nick Kondakis

Addresses: Department of Financial and Management Engineering, University of the Aegean, 8 Michalon Str, Chios 82 100, Greece. ' Department of Shipping, Trade and Transport, University of the Aegean, 2A Korai Str, Chios 82 100, Greece. ' Kepler Asset Management LLC, 100 North Country Road, Setauket, NY 11733, USA

Abstract: We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time variation in risk and correlation between stocks returns and systematic factors. For this reason, variants of the multivariate GARCH models are employed to capture the dynamics in means, variances and covariances of the FF factors structure. Based on these models, we derive optimal capital allocation strategies in the framework of Markowitz|s mean-variance portfolio theory. We outline and compare the out-of-sample performance of these mean-variance allocations with those obtained using simpler techniques, such as sample historical and exponentially weighted moving average (EWMA) estimates.

Keywords: Eugene Fama; Kenneth French; three-factor models; multivariate volatility models; conditional correlation models; portfolio allocation; dynamic factor models; stock portfolios; parametrisation; time variations; risk; stocks returns; systematic factors; generalised autoregressive conditional heteroskedasticity; GARCH models; variance; covariance; capital allocation; mean-variance portfolio theory; Harry Markowitz; out-of-sample performance; exponentially weighted moving average; EWMA; historical estimates; optimisation.

DOI: 10.1504/IJFMD.2010.035764

International Journal of Financial Markets and Derivatives, 2010 Vol.1 No.4, pp.352 - 370

Published online: 03 Oct 2010 *

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