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Endogenous vs Exogenous Instability: An Out-of-Sample Comparison

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  • Domenico Delli Gatti
  • Filippo Gusella
  • Giorgio Ricchiuti

Abstract

Given the unobserved nature of expectations, this paper employs latent variable analysis to examine three financial instability models and assess their out-of-sample forecasting accuracy. We compare a benchmark linear random walk model, which implies exogenous instability phenomena, with a linear state-space model and a nonlinear Markov regime-switching model, both of which postulate endogenous fluctuations phenomena due to heterogeneous behavioral heuristics. Using the S&P 500 dataset from 1990 to 2019, results confirm complex endogenous dynamics and suggest that the inclusion of behavioral nonlinearities improves the model’s predictability both in the short, medium, and long run.

Suggested Citation

  • Domenico Delli Gatti & Filippo Gusella & Giorgio Ricchiuti, 2024. "Endogenous vs Exogenous Instability: An Out-of-Sample Comparison," CESifo Working Paper Series 11082, CESifo.
    • Handle: RePEc:ces:ceswps:_11082
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    1. 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.
    2. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena, 2019. "The term structure of cross-sectional dispersion of expectations in a Learning-to-Forecast Experiment," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 491-520, September.
    3. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    4. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena & Mauro Gallegati, 2020. "Long-run expectations in a learning-to-forecast experiment: a simulation approach," Journal of Evolutionary Economics, Springer, vol. 30(1), pages 75-116, January.
    5. Annarita Colasante & Simone Alfarano & Eva Camacho & Mauro Gallegati, 2018. "Long-run expectations in a learning-to-forecast experiment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(10), pages 681-687, June.
    6. William A. Brock & Cars H. Hommes, 2001. "A Rational Route to Randomness," Chapters, in: W. D. Dechert (ed.), Growth Theory, Nonlinear Dynamics and Economic Modelling, chapter 16, pages 402-438, Edward Elgar Publishing.
    7. 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.
    8. Wai-Mun Chia & Mengling Li & Huanhuan Zheng, 2017. "Behavioral heterogeneity in the Australian housing market," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 872-885, February.
    9. 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.
    10. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    11. Vigfusson, Robert, 1997. "Switching between Chartists and Fundamentalists: A Markov Regime-Switching Approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 2(4), pages 291-305, October.
    12. 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.
    13. Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    14. Bao, Te & Hennequin, Myrna & Hommes, Cars & Massaro, Domenico, 2020. "Coordination on bubbles in large-group asset pricing experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    16. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena, 2020. "Heuristic Switching Model and Exploration-Exploitation Algorithm to Describe Long-Run Expectations in LtFEs: a Comparison," Computational Economics, Springer;Society for Computational Economics, vol. 56(3), pages 623-658, October.
    17. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    18. 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.
    19. Beja, Avraham & Goldman, M Barry, 1980. "On the Dynamic Behavior of Prices in Disequilibrium," Journal of Finance, American Finance Association, vol. 35(2), pages 235-248, May.
    20. 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.
    21. Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 7, pages 327-412, Elsevier.
    22. 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.
    23. 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.
    24. Xiaoping Li & Chunyang Zhou & Chongfeng Wu, 2013. "The role of heterogeneous expectations in forward exchange market," Applied Economics Letters, Taylor & Francis Journals, vol. 20(5), pages 471-475, March.
    25. 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.
    26. 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.
    27. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    28. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
    29. 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.
    30. 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.
    31. Schmitt, Noemi, 2021. "Heterogeneous Expectations And Asset Price Dynamics," Macroeconomic Dynamics, Cambridge University Press, vol. 25(6), pages 1538-1568, September.
    32. Frijns, Bart & Zwinkels, Remco C.J., 2018. "Time-varying arbitrage and dynamic price discovery," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 485-502.
    33. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
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    More about this item

    Keywords

    endogenous instability; exogenous instability; behavioral model; forecasting analysis;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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