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Mean-field type forward-backward doubly stochastic differential equations and related stochastic differential games |
Qingfeng ZHU1,2, Lijiao SU1, Fuguo LIU3, Yufeng SHI2(), Yong’ao SHEN1, Shuyang WANG4 |
1. School of Mathematics and Quantitative Economics, Shandong University of Finance and Economics, and Shandong Key Laboratory of Blockchain Finance, Jinan 250014, China 2. Institute for Financial Studies and School of Mathematics, Shandong University, Jinan 250100, China 3. Department of Mathematics, Changji University, Changji 831100, China 4. School of Informatics, Xiamen University, Xiamen 361005, China |
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Abstract We study a kind of partial information non-zero sum differential games of mean-field backward doubly stochastic differential equations, in which the coefficient contains not only the state process but also its marginal distribution, and the cost functional is also of mean-field type. It is required that the control is adapted to a sub-filtration of the filtration generated by the underlying Brownian motions. We establish a necessary condition in the form of maximum principle and a verification theorem, which is a sufficient condition for Nash equilibrium point. We use the theoretical results to deal with a partial information linear-quadratic (LQ) game, and obtain the unique Nash equilibrium point for our LQ game problem by virtue of the unique solvability of mean-field forward-backward doubly stochastic differential equation.
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Keywords
Non-zero sum stochastic differential game
mean-field
backward doubly stochastic differential equation (BDSDE)
Nash equilibrium point
aximum principle
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Corresponding Author(s):
Yufeng SHI
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Issue Date: 05 February 2021
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