宏观审慎政策应该是反周期的吗?

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2023-10-23 DOI:10.1016/j.jedc.2023.104765
Yoske Igarashi, Keqing Liu
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引用次数: 0

摘要

本文研究了可能的银行违约和银行杠杆约束对货币和宏观审慎政策处方的影响。我们建立了一个新凯恩斯主义模型,银行将资金从家庭引导到企业。银行面临内生杠杆约束,并面临代价高昂的违约。我们将我们的模型与美国经济进行了校准,并表明在去中心化均衡中,银行借贷超过了社会效率水平。限制银行杠杆的宏观审慎政策降低了银行违约风险,提高了长期福利。从短期来看,“宏观审慎风格”的货币政策可以通过影响银行影子价值来减少金融传播,而逆周期资本监管对稳定银行杠杆率是有效的。虽然这两项政策都是有效的,但我们的研究表明,如果货币政策已经被用来缓解金融加速,那么在银行资本监管中引入逆周期性对福利的改善作用甚微。共同最优政策表明,政策制定者应将反周期宏观审慎角色分配给货币政策,银行资本监管应侧重于预期的审慎水平。
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Should macroprudential policy be countercyclical?

This paper investigates the influence of possible bank default and bank leverage constraints on monetary and macroprudential policy prescriptions. We build a New Keynesian model with banks that channel funds from households to firms. Banks face endogenous leverage constraints and are subject to costly default. We calibrate our model to the US economy and show that in the decentralized equilibrium, banks borrow more than the socially efficient level. A macroprudential policy that limits bank leverage reduces the risk of bank default and improves long-run welfare. In the short run, a “macroprudential-flavored” monetary policy can reduce financial propagation by affecting bank shadow values, while countercyclical capital regulation is effective for stabilizing bank leverage. While both policies are effective, our study shows that introducing countercyclicality to bank capital regulation achieves little welfare improvement if monetary policy is already used to mitigate financial acceleration. The jointly optimal policies suggest that policymakers should assign countercyclical macroprudential roles to monetary policy, and bank capital regulation should focus on the desired level of prudence.

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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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