Systemic risk in banking, fire sales, and macroeconomic disasters

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2024-11-01 DOI:10.1016/j.jedc.2024.104975
Spiros Bougheas , David I. Harvey , Alan Kirman , Douglas Nelson
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Abstract

We develop a dynamic computational network model of the banking system where fire sales provide the amplification mechanism of financial shocks. Each period a finite number of banks offers a large, but finite, number of loans to households. Banks with excess liquidity also offer loans to other banks with insufficient liquidity. Thus, each period an interbank loan market is endogenously formed. Bank assets are hit by idiosyncratic shocks drawn from a thin tailed distribution. The uneven distribution of shocks across banks implies that each period there are banks that become insolvent. If insolvent banks happen also to be heavily indebted to other banks, their liquidation can trigger other bank failures. We find that the distribution across time of the growth rate of banking assets has a ‘fat left tail’ that corresponds to rare economic disasters. We also find that the distribution of initial shocks is not a perfect predictor of economic activity; that is some of the uncertainty is endogenous and related to the structure of the interbank network.
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银行业的系统性风险、火灾销售和宏观经济灾难
我们建立了一个银行系统的动态计算网络模型,在这个模型中,火灾销售提供了金融冲击的放大机制。每一时期,有限数量的银行向家庭提供大量但有限的贷款。流动性过剩的银行也会向其他流动性不足的银行提供贷款。因此,每个时期都会内生性地形成一个银行间贷款市场。银行资产受到来自细尾分布的特异性冲击。冲击在银行间的不均匀分布意味着每期都会有银行破产。如果资不抵债的银行恰好也对其他银行负债累累,那么它们的清算就会引发其他银行的倒闭。我们发现,银行资产增长率的跨期分布有一个 "肥大的左尾",与罕见的经济灾难相对应。我们还发现,初始冲击的分布并不能完美预测经济活动;也就是说,有些不确定性是内生的,与银行间网络的结构有关。
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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