Mean variance and goal achieving portfolio for discrete-time market with currently observable source of correlations

The paper studies optimal portfolio selection for discrete time market models in mean-variance and goal achieving setting. The optimal strategies are obtained for models with an observed process that causes serial correlations of price changes. The optimal strategies are found to be myopic for the goal-achieving problem and quasi-myopic for the mean variance portfolio

Discrete time market with serial correlations and optimal myopic strategies.

The paper studies discrete time market models with serial correlations. We found a market structure that ensures that the optimal strategy is myopic for the case of both power or logutility function. In addition, discrete time approximation of optimal continuous time strategies for diffusion market is analyzed. It is found that the performance of optimal myopic diffusion strategies cannot be approximated by optimal strategies with discrete time transactions that are optimal for the related discrete time market model