This paper investigates optimal portfolio strategies in a market where the drift is driven by an unobserved Markov chain. Information on the state of this chain is obtained from stock prices and expert opinions in the form of signals at random discrete time points. As in Frey et al. (2012), Int. J. Theor. Appl. Finance, 15, No. 1, we use stochastic filtering to transform the original problem into an optimization problem under full information where the state variable is the filter for the Markov chain. This problem is studied with dynamic programming techniques and with regularization arguments . Using results from the recent literature we obtain the existence of classical solutions to the dynamic programming equation in a regularized version of the model. From this the optimal strategy in the regularized model is straightforward to compute. We give convergence results which show that this strategy is epsilon-optimal in the original model .
This paper investigates optimal portfolio strategies in a market where the drift is driven by an unobserved Markov chain. Information on the state of this chain is obtained from stock prices and expert opinions in the form of signals at random discrete time points. As in Frey et al. (2012), Int. J. Theor. Appl. Finance, 15, No. 1, we use stochastic filtering to transform the original problem into an optimization problem under full information where the state variable is the filter for the Markov chain. The dynamic programming equation for this problem is studied with viscosity-solution techniques and with regularization arguments .
R
This
The dynamic programming equation for this
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dynamic programming
viscosity-solution
D
. Using results from the recent literature we obtain the existence of classical solutions to the dynamic programming equation in a regularized version of the model. From this the optimal strategy in the regularized model is straightforward to compute. We give convergence results which show that this strategy is epsilon-optimal in the original model