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BUBBLES, CRASHES, AND THE FINANCIAL CYCLE: THE IMPACT OF BANKING REGULATION ON DEEP RECESSIONS

Published online by Cambridge University Press:  19 December 2017

Sander van der Hoog*
Affiliation:
Bielefeld University
Herbert Dawid
Affiliation:
Bielefeld University
*
Address correspondence to: Sander van der Hoog, Chair for Economic Theory and Computational Economics (ETACE), Department of Business Administration and Economics, Bielefeld University, Universitaetsstrasse 25, D-33615 Bielefeld, Germany; e-mail: [email protected].

Abstract

This paper explores how different credit market and banking regulations affect business fluctuations. Capital adequacy- and reserve requirements are analyzed for their effect on the risk of severe downturns. We develop an agent-based macroeconomic model in which financial contagion is transmitted through balance sheets in an endogenous firm-bank network, which incorporates firm bankruptcy and heterogeneity among banks to capture the fact that contagion effects are bank specific. Using concepts from the empirical literature to identify amplitude and duration of recessions and expansions, we show that more stringent liquidity regulations are best to dampen output fluctuations and prevent severe downturns. Under such regulations, both leverage along expansions and amplitude of recessions become smaller. More stringent capital requirements induce larger output fluctuations and lead to deeper, more fragile recessions. This indicates that the capital adequacy requirement is procyclical and therefore not advisable as a measure to prevent financial contagion.

Type
Articles
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

We gratefully acknowledge Philipp Harting and Simon Gemkow for their substantial contributions to the development and implementation of the Eurace@Unibi model [Gemkow et al. (May 2014)] and the associated R scripts [Gemkow and van der Hoog (2012)] for data analysis, for which we have made use of software provided by the R Project [R Development Core Team (2008)].

Simulations were performed using the Flexible Large-scale Agent Modelling Environment (FLAME, see www.flame.ac.uk), using the FLAME Xparser and Libmboard library [Coakley et al. (2012)] that is made available under the Lesser General Public License (LGPL v3). FLAME can be obtained from: https://github.com/FLAME-HPC. The code for reproducing the results in this paper is available from the data publication van der Hoog and Dawid (2016). To run the model and perform policy simulations the ETACE Virtual Appliance can be downloaded from: http://www.wiwi.uni-bielefeld.de/lehrbereiche/vwl/etace/Eurace_Unibi/Virtual_Appliance.

We furthermore thank the Regional Computing Center of the University of Cologne (RRZK) for providing computing time on the DFG-funded High Performance Computing (HPC) system CHEOPS as well as support.

We are grateful for comments by two anonymous referees. The paper has also benefited from comments and suggestions by the participants of the CEF Conference 2013 in Vancouver, the CeNDEF@15 Symposium in Amsterdam, the 1st Workshop on Agent-based Macroeconomics in Bordeaux, the CEF Conference 2014 in Oslo, the Post-Keynesian Economics Conference in Kansas City 2014, the Conference of the Eastern Economics Association in New York City 2015, as well as by the members of the Financial Stability Department of the Bank of Canada. This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 649186—Project ISIGrowth (Innovation-fuelled, Sustainable, Inclusive Growth).

References

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