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How banks’ strategies influence financial cycles: An approach to identifying micro behavior

Author

Listed:
  • Simone Berardi

    (LEE and Department of Economics, Universitat Jaume I, Castellón, Spain
    Department of Management, Università Politecnica delle Marche, Italy)

  • Gabriele Tedeschi

    (LEE and Department of Economics, Universitat Jaume I, Castellón, Spain)

Abstract

In this paper, we show that the values of parameters of a well-calibrated model are useful in detecting micro behavior. We use a calibration procedure suitable for validating agent-based models to show how the evolution of model parameters, obtained via a rolling window estimation, illustrates the evolution of agents’ strategies in response to different economic conditions. In this regard, we calibrate the well-known financial model of Brock and Hommes using three banking indices (i.e., the S&P SmallCap 600 Financials Index, the STOXX Europe 600 Banks, and the STOXX Asia/Pacific 600 Banks) running from 1994 to 2016. The choice of a spatially and temporally diversified dataset allows us to analyze differences and similarities in the behavior of banks belonging to the different macro areas, as well as to capture agents’ reaction to the several economic phases characterizing the time series investigated.

Suggested Citation

  • Simone Berardi & Gabriele Tedeschi, 2016. "How banks’ strategies influence financial cycles: An approach to identifying micro behavior," Working Papers 2016/24, Economics Department, Universitat Jaume I, Castellón (Spain).
  • Handle: RePEc:jau:wpaper:2016/24
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    as
    1. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    2. Recchioni, Maria Cristina & Tedeschi, Gabriele & Gallegati, Mauro, 2015. "A calibration procedure for analyzing stock price dynamics in an agent-based framework," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 1-25.
    3. Akin, Ozlem & Marín, José María & Peydró, José-Luis, 2020. "Anticipating the financial crisis: Evidence from insider trading in banks," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 213-267.
    4. Ozgür Orhangazi, 2008. "Financialisation and capital accumulation in the non-financial corporate sector:," Cambridge Journal of Economics, Oxford University Press, vol. 32(6), pages 863-886, November.
    5. C. H. Hommes, 2001. "Financial markets as nonlinear adaptive evolutionary systems," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 149-167.
    6. Bao, Te & Duffy, John & Hommes, Cars, 2013. "Learning, forecasting and optimizing: An experimental study," European Economic Review, Elsevier, vol. 61(C), pages 186-204.
    7. Kalemli-Ozcan, Sebnem & Papaioannou, Elias & Perri, Fabrizio, 2013. "Global banks and crisis transmission," Journal of International Economics, Elsevier, vol. 89(2), pages 495-510.
    8. Kirman Alan & Teyssière Gilles, 2002. "Microeconomic Models for Long Memory in the Volatility of Financial Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(4), pages 1-23, January.
    9. Ruggero Grilli & Gabriele Tedeschi & Mauro Gallegati, 2015. "Markets connectivity and financial contagion," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 287-304, October.
    10. Ghonghadze, Jaba & Lux, Thomas, 2015. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," FinMaP-Working Papers 38, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    11. Tedeschi, Gabriele & Iori, Giulia & Gallegati, Mauro, 2012. "Herding effects in order driven markets: The rise and fall of gurus," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 82-96.
    12. Hüsler, A. & Sornette, D. & Hommes, C.H., 2013. "Super-exponential bubbles in lab experiments: Evidence for anchoring over-optimistic expectations on price," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 304-316.
    13. W. Brian Arthur, 1994. "Inductive Reasoning, Bounded Rationality and the Bar Problem," Working Papers 94-03-014, Santa Fe Institute.
    14. Ben S. Bernanke & Mark Gertler, 1995. "Inside the Black Box: The Credit Channel of Monetary Policy Transmission," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 27-48, Fall.
    15. Boswijk, H. Peter & Hommes, Cars H. & Manzan, Sebastiano, 2007. "Behavioral heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1938-1970, June.
    16. Ben Bernanke & Mark Gertler, 1990. "Financial Fragility and Economic Performance," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(1), pages 87-114.
    17. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.
    18. Agliari, Anna & Hommes, Cars H. & Pecora, Nicolò, 2016. "Path dependent coordination of expectations in asset pricing experiments: A behavioral explanation," Journal of Economic Behavior & Organization, Elsevier, vol. 121(C), pages 15-28.
    19. Zhenxi Chen & Thomas Lux, 2018. "Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 711-744, October.
    20. Grilli, Ruggero & Tedeschi, Gabriele & Gallegati, Mauro, 2014. "Bank interlinkages and macroeconomic stability," International Review of Economics & Finance, Elsevier, vol. 34(C), pages 72-88.
    21. Haile, Fasika & Pozo, Susan, 2008. "Currency crisis contagion and the identification of transmission channels," International Review of Economics & Finance, Elsevier, vol. 17(4), pages 572-588, October.
    22. Manzan, Sebastiano & Westerhoff, Frank H., 2007. "Heterogeneous expectations, exchange rate dynamics and predictability," Journal of Economic Behavior & Organization, Elsevier, vol. 64(1), pages 111-128, September.
    23. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    24. de Jong, Eelke & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2010. "Heterogeneity of agents and exchange rate dynamics: Evidence from the EMS," Journal of International Money and Finance, Elsevier, vol. 29(8), pages 1652-1669, December.
    25. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-411, May.
    26. Morris Goldstein & Daniel Xie, 2009. "The impact of the financial crisis on emerging Asia," Proceedings, Federal Reserve Bank of San Francisco, issue Oct, pages 27-80.
    27. Cars Hommes & Joep Sonnemans & Jan Tuinstra & Henk van de Velden, 2005. "Coordination of Expectations in Asset Pricing Experiments," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 955-980.
    28. G. Tedeschi & G. Iori & M. Gallegati, 2009. "The role of communication and imitation in limit order markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 489-497, October.
    29. Akin, Ozlem & Marín, José María & Peydró, José-Luis, 2020. "Anticipating the financial crisis: Evidence from insider trading in banks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 35(102), pages 213-267.
    30. Gabriele Tedeschi & Amin Mazloumian & Mauro Gallegati & Dirk Helbing, 2012. "Bankruptcy Cascades in Interbank Markets," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-10, December.
    31. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    32. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    33. Gilli, M. & Winker, P., 2003. "A global optimization heuristic for estimating agent based models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 299-312, March.
    34. Louis-Philippe Rochon & Sergio Rossi, 2010. "Has "It" Happened Again?," International Journal of Political Economy, Taylor & Francis Journals, vol. 39(2), pages 5-9.
    35. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    36. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    37. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    38. de Jong, Eelke & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2009. "Behavioural heterogeneity and shift-contagion: Evidence from the Asian crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1929-1944, November.
    39. Gatti, Domenico Delli & Guilmi, Corrado Di & Gaffeo, Edoardo & Giulioni, Gianfranco & Gallegati, Mauro & Palestrini, Antonio, 2005. "A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility," Journal of Economic Behavior & Organization, Elsevier, vol. 56(4), pages 489-512, April.
    40. Blake LeBaron & Ryuichi Yamamoto, 2008. "The Impact of Imitation on Long Memory in an Order-Driven Market," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 504-517.
    41. Stefan Reitz & Ulf Slopek, 2009. "Non‐Linear Oil Price Dynamics: A Tale of Heterogeneous Speculators?," German Economic Review, Verein für Socialpolitik, vol. 10(3), pages 270-283, August.
    42. Stigler, George J & Becker, Gary S, 1977. "De Gustibus Non Est Disputandum," American Economic Review, American Economic Association, vol. 67(2), pages 76-90, March.
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    Cited by:

    1. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    2. Kukacka, Jiri & Barunik, Jozef, 2017. "Estimation of financial agent-based models with simulated maximum likelihood," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 21-45.

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    More about this item

    Keywords

    Microfoundations; validation; agent-based models; heterogeneous beliefs;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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