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多重风险暴露下动态银行系统风险积累

Dynamic Banking Systemic Risk Accumulation under Multiple-Risk Exposures.

作者信息

Fan Hong, Tang Miao

机构信息

Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China.

出版信息

Entropy (Basel). 2022 Dec 19;24(12):1848. doi: 10.3390/e24121848.

Abstract

Much of the existing research on banking systemic risk focuses on static single-risk exposures, and there is a lack of research on multiple-risk exposures. The reality is that the banking system is facing an increasingly complex environment, and dynamic measures of multiple-risk integration are essential. To reveal the risk accumulation process under the multi-risk exposures of the banking system, this article constructs a dynamic banking system as the research object and combines geometric Brownian motion, the BSM model, and the maximum likelihood estimate method. This article also aims to incorporate three types of exposures (interbank lending market risk exposures, entity industry credit risk exposures, and market risk exposures) within the same framework for the first time and builds a model of the dynamic evolution of banking systemic risk under multiple exposures. This study included the collection of a large amount of real data on banks, entity industries, and market risk factors, and used the ΔCoVaR model to evaluate the systemic risk of the China banking system from the point of view of the accumulation of risk from different exposures, revealing the dynamic process of risk accumulation under the integration of multiple risks within the banking system, as well as the contribution of different exposures to banking systemic risk. The results showed that the banking systemic risk of China first increased and then decreased with time, and the rate of risk accumulation is gradually slowing down. In terms of the impact of different kinds of exposures on system losses, the credit risk exposure of the entity industry had the greatest impact on the banking systemic risk among the three kinds of exposures. In terms of the contribution of the interbank lending market risk to the systemic risk, the Bank of Communications, China Everbright Bank, and Bank of Beijing contributed the most. In terms of the contribution of the bank-entity industry credit risk to the systemic risk, the financial industry, accommodation and catering industry, and manufacturing industry contributed the most. Considering the contribution of market risk to the systemic risk, the Shanghai Composite Index, the Hang Seng Composite Index, and the Dow Jones Index contributed the most. The research in this paper enriches the existing banking systemic risk research perspective and provides a reference for the regulatory decisions of central banks.

摘要

现有的关于银行系统性风险的研究大多集中在静态单一风险敞口上,而对多重风险敞口的研究较少。现实情况是,银行体系面临的环境日益复杂,多重风险整合的动态测度至关重要。为揭示银行体系多重风险敞口下的风险积累过程,本文构建了一个动态银行体系作为研究对象,并结合几何布朗运动、布莱克-斯科尔斯-默顿模型(BSM模型)和最大似然估计方法。本文还旨在首次将三种类型的敞口(银行间同业拆借市场风险敞口、实体产业信用风险敞口和市场风险敞口)纳入同一框架,并构建多重敞口下银行系统性风险的动态演化模型。本研究收集了大量关于银行、实体产业和市场风险因素的真实数据,并使用ΔCoVaR模型从不同敞口的风险积累角度评估中国银行业体系的系统性风险,揭示了银行体系内多重风险整合下风险积累的动态过程,以及不同敞口对银行系统性风险的贡献。结果表明,中国的银行系统性风险随时间先上升后下降,且风险积累速度逐渐放缓。在不同类型敞口对系统损失的影响方面,实体产业的信用风险敞口在三种敞口中对银行系统性风险的影响最大。在银行间同业拆借市场风险对系统性风险的贡献方面,交通银行、中国光大银行和北京银行的贡献最大。在银行-实体产业信用风险对系统性风险的贡献方面,金融业、住宿和餐饮业以及制造业的贡献最大。考虑市场风险对系统性风险的贡献,上证综合指数、恒生综合指数和道琼斯指数的贡献最大。本文的研究丰富了现有的银行系统性风险研究视角,为央行的监管决策提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d52/9778045/246355e017eb/entropy-24-01848-g001.jpg

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