The description of thedynamic behavior of multiple time series represents an important point of departure to obtain accurate forecasts both in economic and financial analysis. The aim of this work isthe comparison of methods whichexploit serial dependence in stock returns to improve out-of-sample portfolio performance.For multivariate time series, the popular and easy-to-use Vector AutoRegressive (VAR) model is compared to some copula models which allow for a non-linear and/or asymmetric dependencestructure among the variables.After deriving theVAR-based and copula-based conditional expected returns, we construct different portfolios and compare them in terms of Sharpe ratio.