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Detecting And Measuring Financial Cycles In Heterogeneous Agents Models: An Empirical Analysis

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  • FILIPPO GUSELLA

    (Università degli Studi di Firenze, Firenze, Italy2Complexity Lab in Economics (CLE), Università Cattolica del Sacro Cuore, Milano, Italy)

Abstract

This paper proposes a macroeconometric analysis to depict and measure possible financial cycles that emerge due to the dynamic interaction between heterogeneous market participants. We consider two-type heterogeneous speculative agents: Trend followers tend to follow the price trend while contrarians go against the wind. As agents’ beliefs are unobserved variables, we construct a state-space model where heuristics are considered as unobserved state components and from which the conditions for endogenous cycles can be mathematically derived and empirically tested. Further, we specifically measure the length of endogenous financial cycles. The model is estimated using the equity price index for the 1960–2020 period for the UK, France, Germany, Italy, Ireland, and the USA. We find empirical evidence of endogenous financial cycles for all countries, with the highest frequencies in the USA and the UK.

Suggested Citation

  • Filippo Gusella, 2022. "Detecting And Measuring Financial Cycles In Heterogeneous Agents Models: An Empirical Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 25(02n03), pages 1-22, March.
    • Handle: RePEc:wsi:acsxxx:v:25:y:2022:i:02n03:n:s0219525922400021
    DOI: 10.1142/S0219525922400021
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    1. Galati, Gabriele & Hindrayanto, Irma & Koopman, Siem Jan & Vlekke, Marente, 2016. "Measuring financial cycles in a model-based analysis: Empirical evidence for the United States and the euro area," Economics Letters, Elsevier, vol. 145(C), pages 83-87.
    2. Paul De Grauwe, 2014. "Booms and Busts in Economic Activity: A Behavioral Explanation," World Scientific Book Chapters, in: Exchange Rates and Global Financial Policies, chapter 19, pages 521-556, World Scientific Publishing Co. Pte. Ltd..
    3. 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.
    4. 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.
    5. Jasper de Winter & Siem Jan Koopman & Irma Hindrayanto & Anjali Chouhan, 2017. "Modeling the business and financial cycle in a multivariate structural time series model," DNB Working Papers 573, Netherlands Central Bank, Research Department.
    6. 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.
    7. Hommes, Cars & in ’t Veld, Daan, 2017. "Booms, busts and behavioural heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 101-124.
    8. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2019. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 541-569, September.
    9. Lux, Thomas, 2018. "Estimation of agent-based models using sequential Monte Carlo methods," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 391-408.
    10. Thomas Lux, 2021. "Can heterogeneous agent models explain the alleged mispricing of the S&P 500?," Quantitative Finance, Taylor & Francis Journals, vol. 21(9), pages 1413-1433, September.
    11. Cars Hommes, 2021. "Behavioral and Experimental Macroeconomics and Policy Analysis: A Complex Systems Approach," Journal of Economic Literature, American Economic Association, vol. 59(1), pages 149-219, March.
    12. Chiarella, Carl & He, Xue-Zhong & Huang, Weihong & Zheng, Huanhuan, 2012. "Estimating behavioural heterogeneity under regime switching," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 446-460.
    13. ter Ellen, Saskia & Hommes, Cars H. & Zwinkels, Remco C.J., 2021. "Comparing behavioural heterogeneity across asset classes," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 747-769.
    14. 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.
    15. Saskia ter Ellen & Willem F. C. Verschoor, 2018. "Heterogeneous Beliefs and Asset Price Dynamics: A Survey of Recent Evidence," Dynamic Modeling and Econometrics in Economics and Finance, in: Fredj Jawadi (ed.), Uncertainty, Expectations and Asset Price Dynamics, pages 53-79, Springer.
    16. 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.
    17. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2006. "Estimation of a simple agent-based model of financial markets: An application to Australian stock and foreign exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 38-42.
    18. Franke, Reiner & Westerhoff, Frank, 2012. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1193-1211.
    19. Borio, Claudio, 2014. "The financial cycle and macroeconomics: What have we learnt?," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 182-198.
    20. Roberto Veneziani & Luca Zamparelli & 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.
    21. Fredj Jawadi (ed.), 2018. "Uncertainty, Expectations and Asset Price Dynamics," Dynamic Modeling and Econometrics in Economics and Finance, Springer, number 978-3-319-98714-9, June.
    22. Simon, Herbert A., 1984. "On the behavioral and rational foundations of economic dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 5(1), pages 35-55, March.
    23. Rünstler, Gerhard & Balfoussia, Hiona & Burlon, Lorenzo & Buss, Ginters & Comunale, Mariarosaria & De Backer, Bruno & Dewachter, Hans & Guarda, Paolo & Haavio, Markus & Hindrayanto, Irma & Iskrev, Nik, 2018. "Real and financial cycles in EU countries - Stylised facts and modelling implications," Occasional Paper Series 205, European Central Bank.
    24. Delli Gatti, Domenico & Grazzini, Jakob, 2020. "Rising to the challenge: Bayesian estimation and forecasting techniques for macroeconomic Agent Based Models," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 875-902.
    25. Schmitt, Noemi, 2021. "Heterogeneous Expectations And Asset Price Dynamics," Macroeconomic Dynamics, Cambridge University Press, vol. 25(6), pages 1538-1568, September.
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    More about this item

    Keywords

    Heterogeneous agent models; heterogeneous expectations; endogenous cycles; state-space model; period of cycles;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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