不同抵押品类型的金融危机

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2024-07-11 DOI:10.1016/j.jedc.2024.104915
Rohan Shah
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引用次数: 0

摘要

企业以盈利为抵押借款多于以资产为抵押借款。这对金融危机的总体反应有何影响?我采用了一个动态随机一般均衡模型,模型中的异质企业在借款约束下选择资本和债务,并研究了当企业可以使用不同类型的抵押品时,金融危机后的复苏情况。我比较了两种抵押类型:资产和收益。我发现,当企业以盈利为抵押借款时,衰退程度更深,但与以资产为抵押借款时相比,复苏速度更快。我还发现,这两种抵押品本身都不能完全解释大衰退后的复苏。相反,2007-2008 年金融危机后,企业以盈利为抵押借款比企业以资产为抵押借款更能反映投资的路径,但在研究产出路径时,这种情况正好相反。这表明,需要结合多种抵押类型才能完全反映大衰退后的复苏情况。
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Financial crises with different collateral types

Firms borrow against earnings more than they do against their assets. How does this affect the aggregate response to financial crises? I take a dynamic stochastic general equilibrium model of heterogeneous firms that choose their capital and debt subject to a borrowing constraint and examine the recovery from a financial crisis when firms can use different types of collateral. I compare between two collateral types: assets, and earnings. I find that when firms borrow against earnings recessions are deeper, but recoveries are quicker compared to when firms borrow against assets. I also find that neither type of collateral can, by itself, completely explain the recovery from the Great Recession. Instead, the path of investment after the 2007-2008 Financial crisis is better captured by firms borrowing against earnings than by firms borrowing against assets, but this is reversed when looking at the path of output. This suggests that a combination of collateral types is required to fully capture the recovery from the Great Recession.

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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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