Abstracts

We investigate optimal portfolio strategies for utility maximizing investors 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. These signals we model by a marked point process with jump-size distribution depending on the current state of the hidden Markov chain.
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. For the case of power utility we present results from the numerical solution of the dynamic programming equation.
The talk is based on the following publications:
Frey, R., Gabih, A. and Wunderlich, R. (2012): Portfolio optimization under partial information with expert opinions. International Journal of Theoretical and Applied Finance, 15, No. 1.
Frey, R. and Wunderlich, R. (2013). Dynamic Programming Equations for Portfolio Optimization under Partial Information with Expert Opinions, arXiv:1303.2513.