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A credit cycle model with market sentiments

Author

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  • Kubin, Ingrid
  • Zörner, Thomas O.
  • Gardini, Laura
  • Commendatore, Pasquale

Abstract

This paper extends Matsuyama's endogenous credit cycle model to account for recent findings on the role of credit market sentiments. The benchmark model uses a parsimonious financial friction specification in the form of a pledgeability parameter, which indicates how much of the revenue borrowers can pledge for credit. We endogenize this parameter by introducing behavioral aspects of credit markets. Depending on the current level of net worth the credit market sentiment may change. If a critical net worth threshold is passed, a switch from an optimistic to a pessimistic regime occurs. Lenders’ perception of risk and the pledgeability parameter will vary accordingly. The resulting dynamic law of motion is two-dimensional and discontinuous. We show that switching between beliefs fundamentally affects the stability of the system confirming that changes in credit market sentiments drive volatility. However, we also find instances in which behavioral regime switches have a stabilizing effect.

Suggested Citation

  • Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
  • Handle: RePEc:eee:streco:v:50:y:2019:i:c:p:159-174
    DOI: 10.1016/j.strueco.2019.06.006
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    References listed on IDEAS

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    1. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    2. Christian Hotz‐Behofsits & Florian Huber & Thomas Otto Zörner, 2018. "Predicting crypto‐currencies using sparse non‐Gaussian state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 627-640, September.
    3. Markus K. Brunnermeier & Yuliy Sannikov, 2014. "A Macroeconomic Model with a Financial Sector," American Economic Review, American Economic Association, vol. 104(2), pages 379-421, February.
    4. Matsuyama, Kiminori & Sushko, Iryna & Gardini, Laura, 2016. "Revisiting the model of credit cycles with Good and Bad projects," Journal of Economic Theory, Elsevier, vol. 163(C), pages 525-556.
    5. Pasquale Commendatore & Ingrid Kubin & Pascal Mossay & Iryna Sushko, 2017. "The role of centrality and market size in a four-region asymmetric new economic geography model," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1095-1131, November.
    6. Lux, Thomas, 2009. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 638-655, November.
    7. Thomas Lux, 2009. "Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey," Post-Print hal-00720175, HAL.
    8. David López-Salido & Jeremy C. Stein & Egon Zakrajšek, 2017. "Credit-Market Sentiment and the Business Cycle," The Quarterly Journal of Economics, Oxford University Press, vol. 132(3), pages 1373-1426.
    9. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    10. Moritz Schularick & Alan M. Taylor, 2012. "Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008," American Economic Review, American Economic Association, vol. 102(2), pages 1029-1061, April.
    11. Gao, Lin & Süss, Stephan, 2015. "Market sentiment in commodity futures returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 84-103.
    12. Marco Terrones & Enrique G. Mendoza, 2008. "An Anatomy of Credit Booms; Evidence From Macro Aggregates and Micro Data," IMF Working Papers 08/226, International Monetary Fund.
    13. Shu, Hui-Chu, 2010. "Investor mood and financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 76(2), pages 267-282, November.
    14. Kiminori Matsuyama, 2008. "Aggregate Implications of Credit Market Imperfections," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 1-60, National Bureau of Economic Research, Inc.
    15. Schmitt, Noemi & Tuinstra, Jan & Westerhoff, Frank, 2017. "Side effects of nonlinear profit taxes in an evolutionary market entry model: Abrupt changes, coexisting attractors and hysteresis problems," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 15-38.
    16. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    17. Robert J. Shiller, 2003. "From Efficient Markets Theory to Behavioral Finance," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 83-104, Winter.
    18. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2018. "Diagnostic Expectations and Credit Cycles," Journal of Finance, American Finance Association, vol. 73(1), pages 199-227, February.
    19. Matsuyama, Kiminori, 2013. "The good, the bad, and the ugly: An inquiry into the causes and nature of credit cycles," Theoretical Economics, Econometric Society, vol. 8(3), September.
    20. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272.
    21. Bernanke, Ben & Gertler, Mark, 1989. "Agency Costs, Net Worth, and Business Fluctuations," American Economic Review, American Economic Association, vol. 79(1), pages 14-31, March.
    22. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.),Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    23. Reiner Franke, 2008. "A Microfounded Herding Model and Its Estimation On German Survey Expectations," European Journal of Economics and Economic Policies: Intervention, Edward Elgar Publishing, vol. 5(2), pages 301-328.
    24. Matthew Baron & Wei Xiong, 2017. "Credit Expansion and Neglected Crash Risk," The Quarterly Journal of Economics, Oxford University Press, vol. 132(2), pages 713-764.
    25. Tramontana, Fabio & Westerhoff, Frank & Gardini, Laura, 2013. "The bull and bear market model of Huang and Day: Some extensions and new results," Journal of Economic Dynamics and Control, Elsevier, vol. 37(11), pages 2351-2370.
    26. De Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 95-117.
    27. Reiner Franke & Frank Westerhoff, 2017. "Taking Stock: A Rigorous Modelling Of Animal Spirits In Macroeconomics," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1152-1182, December.
    28. De Luigi, Clara & Huber, Florian, 2018. "Debt regimes and the effectiveness of monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 218-238.
    29. Carl Chiarella & Corrado Di Guilmi & Tianhao Zhi, 2015. "Modelling the "Animal Spirits" of Bank's Lending Behaviour," Working Paper Series 183, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    30. Giovanni Favara, 2012. "Agency Problems and Endogenous Investment Fluctuations," Review of Financial Studies, Society for Financial Studies, vol. 25(7), pages 2301-2342.
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    More about this item

    Keywords

    Credit cycles; Financial frictions; Market sentiments; Behavioral inertia;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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