Systemic risk models for disjoint and overlapping groups with equilibrium strategies

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2022-02-01 DOI:10.1515/strm-2022-0004
Yichen Feng, J. Fouque, Ruimeng Hu, Tomoyuki Ichiba
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引用次数: 1

Abstract

Abstract We analyze the systemic risk for disjoint and overlapping groups of financial institutions by proposing new models with realistic game features. Specifically, we generalize the systemic risk measure proposed in [F. Biagini, J.-P. Fouque, M. Frittelli and T. Meyer-Brandis, On fairness of systemic risk measures, Finance Stoch. 24 (2020), 2, 513–564] by allowing individual banks to choose their preferred groups instead of being assigned to certain groups. We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor. We also provide an explicit solution for the risk allocation of the individual banks and study the existence and uniqueness of Nash equilibrium both theoretically and numerically. The developed numerical algorithm can simulate scenarios of equilibrium, and we apply it to study the banking structure with real data and show the validity of the proposed model.
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具有均衡策略的不相交和重叠群体的系统风险模型
摘要我们通过提出具有现实博弈特征的新模型来分析不相交和重叠的金融机构群体的系统风险。具体而言,我们通过允许个别银行选择自己喜欢的群体,而不是被分配到某些群体,来推广[F.Biagini,J.-P.Fouque,M.Fritelli和T.Meyer Brandis,On fairity of systemic risk measures,Finance Stoch.24(2020),2513–564]中提出的系统性风险测度。我们为这些新模型引入了纳什均衡的概念,并分析了风险因子在高斯分布下的最优解。我们还为单个银行的风险分配提供了一个显式的解决方案,并从理论和数值上研究了纳什均衡的存在性和唯一性。所开发的数值算法可以模拟均衡情景,并将其应用于实际数据的银行结构研究,验证了所提出模型的有效性。
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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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