Portfolio Optimization under Partial Information with Expert Opinions
This paper investigates optimal portfolio strategies in a market with partial information on the drift. The drift is modelled as a function of a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from the observation of st...
| Main Authors: | , , |
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| Format: | Others |
| Language: | en |
| Published: |
World Scientific Publishing
2012
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| Subjects: | |
| Online Access: | http://epub.wu.ac.at/3844/1/Frey.pdf http://dx.doi.org/10.1142/S0219024911006486 |
| Summary: | This paper investigates optimal portfolio strategies in a market with partial information
on the drift. The drift is modelled as a function of a continuous-time Markov chain
with finitely many states which is not directly observable. Information on the drift is
obtained from the observation of stock prices. Moreover, expert opinions in the form
of signals at random discrete time points are included in the analysis. We derive the
filtering equation for the return process and incorporate the filter into the state variables
of the optimization problem. This problem is studied with dynamic programming
methods. In particular, we propose a policy improvement method to obtain computable
approximations of the optimal strategy. Numerical results are presented at the end. (author's abstract) |
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