On the effect of heterogeneity on flocking behavior and systemic risk

IF 1.3 Q2 STATISTICS & PROBABILITY
Fei Fang, Yiwei Sun, K. Spiliopoulos
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引用次数: 4

Abstract

Abstract The goal of this paper is to study organized flocking behavior and systemic risk in heterogeneous mean-field interacting diffusions. We illustrate in a number of case studies the effect of heterogeneity in the behavior of systemic risk in the system, i.e., the risk that several agents default simultaneously as a result of interconnections. We also investigate the effect of heterogeneity on the “flocking behavior” of different agents, i.e., when agents with different dynamics end up following very similar paths and follow closely the mean behavior of the system. Using Laplace asymptotics, we derive an asymptotic formula for the tail of the loss distribution as the number of agents grows to infinity. This characterizes the tail of the loss distribution and the effect of the heterogeneity of the network on the tail loss probability.
异质性对群集行为和系统性风险的影响
摘要本文的目的是研究异质平均场相互作用扩散中有组织的群集行为和系统风险。我们在一些案例研究中说明了系统中系统性风险行为的异质性的影响,即由于相互联系而导致几个代理同时违约的风险。我们还研究了异质性对不同主体的“群集行为”的影响,即当具有不同动力学的主体最终遵循非常相似的路径并密切遵循系统的平均行为时。利用拉普拉斯渐近定理,导出了当智能体数量趋于无穷时,损失分布尾部的渐近公式。这表征了损失分布的尾部,以及网络的异质性对尾部损失概率的影响。
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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