Abstract
Predictions of asset returns and volatilities are heavily discussed and analyzed in the finance research literature. In this paper, we compare linear and nonlinear predictions for stock- and bond index returns and their covariance matrix. We show in-sample and out-of-sample prediction accuracy as well as their impact on asset allocation results for short-horizon investors. Our data comprises returns from the German DAX stock market index and the REXP bond market index as well as their joint covariance matrix over the period 01/1988 - 12/2007. The comparison of a linear and nonlinear prediction approach is the focus of this study. The results show that while out-of-sample prediction accuracies are weak in terms of statistical significance, asset allocation performances based on linear predictions result in significant Jensen's alpha measures and Sharpe-ratio and are further improved by nonlinear predictions.
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