Portfolio allocation using multivariate variance gamma models

In this paper, we investigate empirically the effect of using higher moments in portfolio allocation when parametric and nonparametric models are used. The nonparametric model considered in this paper is the sample approach; the parametric model is constructed assuming multivariate variance gamma (MVG) joint distribution for asset returns.We consider the MVG models proposed by Madan and Seneta (<CitationRef CitationID="CR16">1990</CitationRef>), Semeraro (<CitationRef CitationID="CR23">2008</CitationRef>) and Wang (<CitationRef CitationID="CR24">2009</CitationRef>). We perform an out-of-sample analysis comparing the optimal portfolios obtained using the MVG models and the sample approach. Our portfolio is composed of 18 assets selected from the S&P500 Index and the dataset consists of daily returns observed from 01/04/2000 to 01/09/2011. Copyright Swiss Society for Financial Market Research 2013