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Backward Stochastic Differential Equations with Constraints on the Gains-Process

Jaksa Cvitanic, Ioannis Karatzas and H. Mete Soner
The Annals of Probability
Vol. 26, No. 4 (Oct., 1998), pp. 1522-1551
Stable URL: http://www.jstor.org/stable/2652779
Page Count: 30
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Backward Stochastic Differential Equations with Constraints on the Gains-Process
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Abstract

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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