We study optimal stochastic control problems for non-Markovian stochastic differential equations (SDEs) where the drift, diffusion coefficients and gain functionals are path-dependent, and importantly we do not make any ellipticity assumptions on the SDE. We develop a control randomization approach and prove that the value function can be reformulated under a family of dominated measures on an enlarged filtered probability space. This value function is then characterized by a backward SDE with nonpositive jumps under a single probability measure, which can be viewed as a path-dependent version of the Hamilton-Jacobi-Bellman equation, and an extension to G-expectation.
Randomized and backward SDE representation for optimal control of non-Markovian SDEs / M. Fuhrman, H. Pham. - In: THE ANNALS OF APPLIED PROBABILITY. - ISSN 1050-5164. - 25:4(2015), pp. 2134-2167. [10.1214/14-AAP1045]
Randomized and backward SDE representation for optimal control of non-Markovian SDEs
M. FuhrmanPrimo
;
2015
Abstract
We study optimal stochastic control problems for non-Markovian stochastic differential equations (SDEs) where the drift, diffusion coefficients and gain functionals are path-dependent, and importantly we do not make any ellipticity assumptions on the SDE. We develop a control randomization approach and prove that the value function can be reformulated under a family of dominated measures on an enlarged filtered probability space. This value function is then characterized by a backward SDE with nonpositive jumps under a single probability measure, which can be viewed as a path-dependent version of the Hamilton-Jacobi-Bellman equation, and an extension to G-expectation.File | Dimensione | Formato | |
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