Title

Economic stimulus through bank regulation: Government responses to the COVID-19 crisis

Document Type

Article

Source of Publication

Journal of International Financial Markets Institutions and Money

Publication Date

10-7-2021

Abstract

In this paper, we estimate the effects of the COVID-19 pandemic on the banking system and the real economy and simulate potential policy responses. We combine machine learning algorithms, namely a Random Regression Forest and a Long Short Term Memory neural network, with an agent-based framework to calculate the expected results of the pandemic, according to different scenarios regarding financial stability. We then simulate government responses to this crisis and find that traditional demand and supply stimuli are outperformed by our suggestion of relaxing bank regulation. We examine two alternatives of our suggested policy and find that they result in optimised outcomes for most variables examined. Our findings have important policy implications as authorities are formulating post-crisis recovery plans amidst budgetary constraints.

Publisher

Elsevier

Volume

75

Disciplines

Business

Keywords

Coronavirus, Bank regulation, Crisis management, Machine learning, Agent-based finance

Indexed in Scopus

no

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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