Abstract
In the area of optimal asset allocation, the parameters of the model are often assumed to be deterministic. This is not a realistic assumption since most parameters are not known exactly and therefore, have to be estimated. We consider investment opportunities which are modelled as local geometric Brownian motions. The drift terms of the risky assets are assumed to be affine functions of factors. These factors themselves may be stochastic processes. The investor is assumed to have constant relative risk aversion. The optimal asset allocation under partial information is derived by transforming the problem into a full-information problem, where the solution is well known. The analytical result is empirically tested in a real-world application. In our case, we consider the optimal management of a sector rotation fund
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