A central limit theorem for sets of probability measures

被引：13

作者：

Chen, Zengjing ^{[1
]}

Epstein, Larry G. ^{[2
,3
]}

机构：

[1] Shandong Univ, Sch Math, Shanda Nan Rd 17, Jinan 250100, Shandong, Peoples R China

[2] Boston Univ, Dept Econ, 270 Bay State Rd, Boston, MA 02215 USA

[3] McGill Univ, Dept Econ, 855 Sherbrooke St W Montreal, Quebec City, PQ H3A 2T7, Canada

来源：

STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 2022年 / 152卷

基金：

国家重点研发计划;

关键词：

Model uncertainty; Ambiguity; Central limit theorem; Backward stochastic differential equation; Random walk; Robustness; LARGE NUMBERS; RISK; AMBIGUITY;

D O I：

10.1016/j.spa.2022.07.003

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We prove a central limit theorem for a sequence of random variables whose means are ambiguous and vary in an unstructured way. Their joint distribution is described by a set of (suitably equivalent) probability measures. The limit is defined by a backward stochastic differential equation that can be interpreted as modeling an ambiguous continuous-time random walk.(c) 2022 Elsevier B.V. All rights reserved.

引用

页码：424 / 451

页数：28

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