Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22720
Authors: 
Sennewald, Ken
Year of Publication: 
2005
Series/Report no.: 
Dresden discussion paper in economics 03/05
Abstract: 
The present paper is concerned with the optimal control of stochastic differential equations, where uncertainty stems from one or more independent Poisson processes. Optimal behavior in such a setup (e.g., optimal consumption) is usually determined by employing the Hamilton-Jacobi-Bellman equation. This, however, requires strong assumptions on the model, such as a bounded utility function and bounded coefficients in the controlled differential equation. The present paper relaxes these assumptions. We show that one can still use the Hamilton-Jacobi-Bellman equation as a necessary criterion for optimality if the utility function and the coefficients are linearly bounded. We also derive sufficiency in a verification theorem without imposing any boundedness condition at all. It is finally shown that, under very mild assumptions, an optimal Markov control is optimal even within the class of general controls.
Subjects: 
Stochastic differential equation
Poisson process
Bellman equation
JEL: 
C61
Document Type: 
Working Paper

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