The Annals of Probability

Backward stochastic differential equations with constraints on the gains-process

Jak{\v{s}}a Cvitani{\'c}, Ioannis Karatzas, and H. Mete Soner

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We consider backward stochastic differential equations with convex constraints on the gains (or intensity-of-noise) process. Existence and uniqueness of a minimal solution are established in the case of a drift coefficient which is Lipschitz continuous in the state and gains processes and convex in the gains process. It is also shown that the minimal solution can be characterized as the unique solution of a functional stochastic control-type equation. This representation is related to the penalization method for constructing solutions of stochastic differential equations, involves change of measure techniques, and employs notions and results from convex analysis, such as the support function of the convex set of constraints and its various properties.

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Ann. Probab. Volume 26, Number 4 (1998), 1522-1551.

First available in Project Euclid: 31 May 2002

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Zentralblatt MATH identifier

Primary: 60H10: Stochastic ordinary differential equations [See also 34F05] 93E20: Optimal stochastic control
Secondary: 60G40: Stopping times; optimal stopping problems; gambling theory [See also 62L15, 91A60]

Backward SDEs convex constraints stochastic control


Cvitani{\'c}, Jak{\v{s}}a; Karatzas, Ioannis; Soner, H. Mete. Backward stochastic differential equations with constraints on the gains-process. Ann. Probab. 26 (1998), no. 4, 1522--1551. doi:10.1214/aop/1022855872.

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