This paperstudies a mean-variance portfolio selection problem under partial information with drift uncertainty. It is proved that all the contingent claims in this model are attainable in the sense of Xiong and Zhou. Further, we propose a numerical scheme to approximate the optimal portfolio  . Malliavin calculus and the strong law of large numbers play important roles in this scheme.
In this paper, we study the mean-variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above . Malliavin calculus and the particle representation play important roles in this scheme.
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This paperstudies a
In this paper, we study the
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It is proved that all the contingent claims in this model are attainable in
First we show that
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sense of Xiong and Zhou. Further, we propose a
market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient
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that is the solution of the BSDE mentioned above
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strong law of large numbers
particle representation