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Mean-field backward stochastic differential equations driven by G-Brownian motion and related partial differential equations

机译:由G-Brownian运动和相关部分微分方程驱动的平均场向后转换等式

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In this paper, we study mean-field backward stochastic differential equations driven by G-Brownian motion (G-BSDEs). We first obtain the existence and uniqueness theorem of these equations. In fact, we can obtain local solutions by constructing Picard contraction mapping for Y term on small interval, and the global solution can be obtained through backward iteration of local solutions. Then, a comparison theorem for this type of mean-field G-BSDE is derived. Furthermore, we establish the connection of this mean-field G-BSDE and a nonlocal partial differential equation. Finally, we give an application of mean-field G-BSDE in stochastic differential utility model.
机译:本文研究了由G-Brownian运动(G-BSDES)驱动的平均落后随机微分方程。 我们首先获得这些方程的存在和唯一性定理。 事实上,我们可以通过在小区间隔内构建与y术语的图书收缩映射来获得本地解决方案,并且通过对本地解决方案的后退迭代可以获得全局解决方案。 然后,推导出这种类型的平均场G-BSDE的比较定理。 此外,我们建立了这种平均场G-BSDE的连接和非本地偏微分方程。 最后,我们在随机微分实用新型中施用平均字段G-BSDE。

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