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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  and
Herbert Dawid
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: svdhoog@wiwi.uni-bielefeld.de.
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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.

Keywords

Financial CrisesCredit Market and Banking RegulationsFinancial FragilityAgent-Based Macroeconomics
Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 3 , April 2019 , pp. 1205 - 1246
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

REFERENCES

Admati, A. and Hellwig, M. (2013) The Bankers' New Clothes: What's Wrong with Banking and What to Do about It. West Sussex, UK: The University Press Group.Google Scholar
Argote, L. and Epple, D. (1990) Learning curves in manufacturing. Science 247 (4945), 920–924.Google Scholar
Ashraf, Q., Gershman, B., and Howitt, P. (2011) Banks, Market Organization, and Macroeconomic Performance: An Agent-Based Computational Analysis. NBER working papers 17102, National Bureau of Economic Research.Google Scholar
Axtell, R. (2001) Effects of interaction topology and activation regime in several multi-agent systems. In Moss, S. and Davidsson, P. (eds.), Multi-Agent-Based Simulation, 2nd International Workshop, MABS 2000, Boston, MA, USA, July Revised and Additional Papers, Lecture Notes in Computer Science, vol. 1979, pp. 33–48. Berlin, Heidelberg: Springer.Google Scholar
Basel Committee on Banking Supervision (2013) Basel III phase-in arrangements. Available at: http://www.bis.org/bcbs/basel3/basel3_phase_in_arrangements.pdf, retrieved on 6 January 2015.Google Scholar
Battiston, S., Gatti, D. Delli, Gallegati, M., Greenwald, B., and Stiglitz, J. E. (2012) Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk. Journal of Economic Dynamics and Control 36 (8), 1121–1141.Google Scholar
Berkmen, S. P., Gelos, G., Rennhack, R., and Walsh, J. P. (2012) The global financial crisis: Explaining cross-country differences in the output impact. Journal of International Money and Finance 31 (1), 42–59.Google Scholar
Bernanke, B. S. and Blinder, A. S. (1988) Credit, money, and aggregate demand. American Economic Review 78 (2), 435–39.Google Scholar
Bernanke, B. S. and Fall, M. Gertler (1995) Inside the black box: The credit channel of monetary policy transmission. Journal of Economic Perspectives 9 (4), 27–48.Google Scholar
Bry, G. and Boschan, C. (1971) Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. New York: National Bureau of Economic Research; distributed by Columbia University Press.Google Scholar
Caballero, R. J., Hoshi, T., and Kashyap, A. K. (2008) Zombie lending and depressed restructuring in Japan. American Economic Review 98 (5), 1943–1977.Google Scholar
Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., and Stiglitz, J. E. (2016) Agent based-stock flow consistent macroeconomics: Towards a benchmark model. Journal of Economic Dynamics and Control 69, 375–408.Google Scholar
Calomiris, C. W. (2013) Is a 25% bank equity requirement really a no-brainer? In Danielsson, J. (ed.), Post-Crisis Banking Regulation. Evolution of Economic Thinking as it Happened on Vox. Centre for Economic Policy Research, London, Ch. 2.5, pp. 73–80.Google Scholar
Carroll, C. and Summers, L. (1991) Consumption growth parallels income growth: Some new evidence. In Bernheim, B. and Shoven, J. (eds.), National Saving and Economic Performance, pp. 305–348. Chicago: University of Chicago Press.Google Scholar
Claessens, S., Kose, M. A., and Terrones, M. E. (2012) How do business and financial cycles interact? Journal of International Economics 87 (1), 178–190.Google Scholar
Coakley, S., Chin, L.-S., Holcomb, M., Greenough, C., and Worth, D. (2012) Flexible Large-scale Agent Modelling Environment (FLAME). University of Sheffield and Rutherford Appleton Laboratories, STFC, License: Lesser GPL v3. http://www.flame.ac.uk/.Google Scholar
Dawid, H., Gemkow, S., Harting, P., van der Hoog, S., and Neugart, M. (2012) The Eurace@Unibi Model: An Agent-based Macroeconomic Model for Economic Policy Analysis. Bielefeld Working Papers in Economics and Managment No. 05-2012.Google Scholar
Dawid, H., Gemkow, S., Harting, P., van der Hoog, S., and Neugart, M. (2018a) Agent-based macroeconomic modeling and policy analysis: The Eurace@Unibi model. In Chen, S.-H. and Kaboudan, M. (eds.), Handbook on Computational Economics and Finance (in press). Oxford University Press.Google Scholar
Dawid, H., Harting, P., and Neugart, M. (2014) Economic convergence: Policy implications from a heterogeneous agent model. Journal of Economic Dynamics & Control 44, 54–80.Google Scholar
Dawid, H., Harting, P., van der Hoog, S., and Neugart, M. (2018b) Macroeconomics with Heterogeneous Agent Models: Fostering Transparency, Reproducibility and Replication. Journal of Evolutionary Economics, forthcoming.Google Scholar
Deaton, A. (1991) Saving and liquidity constraints. Econometrica 59, 1221–1248.Google Scholar
Delli Gatti, D., Desiderio, S., Gaffeo, E., Cirillo, P., and Gallegati, M. (2011) Macroeconomics from the Bottom-Up. Berlin: Springer.Google Scholar
Delli Gatti, D., Gaffeo, E., Gallegati, M., Giulioni, G., and Palestrini, A. (2008) Emergent Macroeconomics: An Agent-Based Approach to Business Fluctuations. Berlin: Springer.Google Scholar
Delli Gatti, D. and Gallegati, M. (1992) Imperfect information, corporate finance, debt commitments and business fluctuations. In Fazzari, S. and Papadimitriou, D. (eds.), Financial conditions and Macroeconomic performance: Essays in Honor of Hyman P. Minsky, Ch.9, pp. 133–160. New York: M.E. Sharpe.Google Scholar
Delli Gatti, D., Gallegati, M., Giulioni, G., and Palestrini, A. (2003) Financial fragility, patterns of firms' entry and exit and aggregate dynamics. Journal of Economic Behavior & Organization 51 (1), 79–97.Google Scholar
Dosi, G., Fagiolo, G., Napoletano, M., Roventini, A., and Treibich, T. (2013) Income distribution, credit and fiscal policies in an agent-based Keynesian model. Journal of Economic Dynamics and Control 37, 1598–1625.Google Scholar
Dosi, G., Fagiolo, G., Napoletano, M., Roventini, A., and Treibich, T. (2015) Fiscal and monetary policies in complex evolving economies. Journal of Economic Dynamics and Control 52, 166–189.Google Scholar
Dosi, G., Fagiolo, G., and Roventini, A. (2010) Schumpeter meeting Keynes: A policy-friendly model of endogenous growth and business cycles. Journal of Economic Dynamics and Control 34 (9), 1748–1767.Google Scholar
Douglas, P. H., Hamilton, E. J., Fisher, I., King, W. I., Graham, F. D., and Whittlesey, C. R. (1939). A program for monetary reform. Unpublished draft manuscript.Google Scholar
Gali, J., Gertler, M., and Lopez-Salido, J. D. (2007) Markups, gaps and the welfare costs of business fluctuations. Review of Economics and Statistics 89 (1), 44–59.Google Scholar
Geanakoplos, J. (Jul. 2009) The Leverage Cycle. Cowles Foundation discussion papers 1715, Cowles Foundation for Research in Economics, Yale University.Google Scholar
Gemkow, S., Harting, P., and van der Hoog, S. (May 2014) Eurace@Unibi Model v1.0 Source Code. Bielefeld University, Bielefeld, License: EULA. doi: 10.4119/unibi/2900767.Google Scholar
Gemkow, S. and Hoog, S. van der (2012) R Data Analysis Scripts. Bielefeld University, Bielefeld, Unpublished. License: GPL v3.Google Scholar
Hanson, S. G., Kashyap, A. K., and Stein, J. C. (2011) A macroprudential approach to financial regulation. Journal of Economic Perspectives 25 (1), 3–28.Google Scholar
Harding, D. and Pagan, A. (2002) Dissecting the cycle: A methodological investigation. Journal of Monetary Economics 49 (2), 365–381.Google Scholar
van der Hoog, S. and Dawid, H. (2016) Data for the paper: Bubbles, Crashes and the Financial Cycle. Bielefeld University, Bielefeld, License: ODbL. doi: 10.4119/unibi/2906076.Google Scholar
Huberman, B. and Glance, N. (1993) Evolutionary games and computer simulations. Proceedings of the National Academy of Sciences USA 90, 7716–18.Google Scholar
Jordà, O., Schularick, M., and Taylor, A. M. (2013) When credit bites back. Journal of Money, Credit and Banking 45 (s2), 3–28.Google Scholar
Jordà, O., Schularick, M., and Taylor, A. M. (2015) Leveraged Bubbles. NBER working papers no. 21486, National Bureau of Economic Research, Inc.Google Scholar
Kealhofer, S. (2003) Quantifying credit risk I: Default prediction. Financial Analysts Journal 59 (1), 30–44.Google Scholar
Kindleberger, C. P. (2000) Manias, Panics, and Crashes: A History of Financial Crises, 1st ed (1978). New York: John Wiley and Sons.Google Scholar
Krug, S., Lengnick, M., and Wohltmann, H.-W. (2015) The impact of Basel III on financial (in)stability: An agent-based credit network approach. Quantitative Finance 15, 1917–1932.Google Scholar
LeHeron, E. Heron, E. and Mouakil, T. (2008) A Post-Keynesian stock-flow consistent model for dynamic analysis of monetary policy shock on banking behaviour. Metroeconomica 59 (3), 405–440.Google Scholar
Liu, Y.-F., Andersen, J. V., Frolov, M., and de Peretti, P. (2016) Synchronization in Human Decision Making. Documents de travail du Centre d'Economie de la Sorbonne 16035, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.Google Scholar
Malhotra, N. K. (1984) The use of linear logit models in marketing research. Journal of Marketing Research 21 (1), 20–31.Google Scholar
Mandel, A., Jaeger, C., Fürst, S., Lass, W., Lincke, D., Meissner, F., Pablo-Marti, F., and Wolf, S. (2010) Agent-Based Dynamics in Disaggregated Growth Models. Documents de travail du Centre d'Economie de la Sorbonne 10077, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.Google Scholar
Minsky, H. P. (1978) The Financial Instability Hypothesis: A Restatement. Minsky, Hyman P. archive paper 180, 541–552.Google Scholar
Minsky, H. P. (1986) Stabilizing an Unstable Economy. Yale University Press (2nd edition 2008, McGraw-Hill: New York).Google Scholar
Nagle, T., Hogan, J., and Zale, J. (2011) The Strategy and Tactics of Pricing: A Guide to Growing More Profitably. New Jersey: Pearson Prentice Hall.Google Scholar
R Development Core Team (2008) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: http://www.R-project.orgGoogle Scholar
Ratnovski, L. (2013) How much capital should banks have? In Danielsson, J. (ed.), Post-Crisis Banking Regulation. Evolution of Economic Thinking as it Happened on Vox. Centre for Economic Policy Research, London, Ch. 2.4, pp. 65–72.Google Scholar
Schularick, M. and Taylor, A. M. (April 2012) Credit booms gone bust: Monetary policy, leverage cycles, and financial crises, 1870–2008. American Economic Review 102 (2), 1029–61.Google Scholar
Stock, J. H. and Watson, M. W. (1999) Business cycle fluctuations in us macroeconomic time series. In: Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, vol. 1, Ch.1, pp. 3–64. North-Holland: Elsevier.Google Scholar
Vasicek, O. (1984) Credit Valuation. White paper, Moody's KMV.Google Scholar
Verho, J. (2008) Scars of Recession: The Long-Term Costs of the Finnish Economic Crisis. Working papers 2008/09, IFAU - Institute for Labour Market Policy Evaluation.Google Scholar
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