An Out-of-sample Analysis of Mean-Variance Portfolios with Orthogonal GARCH Factors

In this paper a comparative study is conducted to evaluate the out-of-sample performance of mean-variance portfolios when three different variance models are considered. We use the common framework of orthogonal factors to specify the conditional covariance matrix structure. A key advantage of this approach is that the estimated factors can be modeled as univariate GARCH processes so that we can consider models for which multivariate extensions are not available. We, therefore, compared the Integrated GARCH (IGARCH) with the Exponential GARCH (EGARCH) and Fractionally Integrated Exponential GARCH (FIEGARCH) factor models on the basis of statistical diagnostics, and found the EGARCH model superior when fitted with heavy tailed distributions. We also evaluated out-of sample portfolio performances in terms of efficient frontiers, prediction intervals and turnover, and concluded that the EGARCH and FIEGARCH models provide comparable outcomes which are overall superior to the IGARCH performance. Looking jointly at statistical and economic criterions we conclude that fitting a FIEGARCH model with heavy tailed distributions can generally improve out-of-sample portfolio performances.