IDEAS home Printed from https://ideas.repec.org/p/zbw/fmpwps/63.html
   My bibliography  Save this paper

Estimation of financial agent-based models with simulated maximum likelihood

Author

Listed:
  • Kukacka, Jiri
  • Barunik, Jozef

Abstract

This paper proposes a general computational framework for empirical estimation of financial agent based models, for which criterion functions do not have known analytical form. For this purpose, we adapt a nonparametric simulated maximum likelihood estimation based on kernel methods. Employing one of the most widely analysed heterogeneous agent models in the literature developed by Brock and Hommes (1998), we extensively test properties of the proposed estimator and its ability to recover parameters consistently and efficiently using simulations. Key empirical findings point us to the statistical insignificance of the switching coefficient but markedly significant belief parameters defining heterogeneous trading regimes with superiority of trend-following over contrarian strategies. In addition, we document slight proportional dominance of fundamentalists over trend following chartists in main world markets.

Suggested Citation

  • Kukacka, Jiri & Barunik, Jozef, 2016. "Estimation of financial agent-based models with simulated maximum likelihood," FinMaP-Working Papers 63, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  • Handle: RePEc:zbw:fmpwps:63
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/148062/1/86185828X.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. S. Alfarano & T. Lux & F. Wagner, 2007. "Empirical validation of stochastic models of interacting agents," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 183-187, January.
    2. Matthijs Lof, 2015. "Rational Speculators, Contrarians, and Excess Volatility," Management Science, INFORMS, vol. 61(8), pages 1889-1901, August.
    3. Stefan Reitz & Frank Westerhoff, 2007. "Commodity price cycles and heterogeneous speculators: a STAR–GARCH model," Empirical Economics, Springer, vol. 33(2), pages 231-244, September.
    4. Jozef Barunik & Jiri Kukacka, 2015. "Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 959-973, June.
    5. Wilko Bolt & Maria Demertzis & Cees Diks & Cars Hommes & Marco van der Leij, 2014. "Identifying booms and busts in house prices under heterogeneous expectations," DNB Working Papers 450, Netherlands Central Bank, Research Department.
    6. Kristensen, Dennis & Shin, Yongseok, 2012. "Estimation of dynamic models with nonparametric simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 167(1), pages 76-94.
    7. 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.
    8. Kristensen, Dennis, 2009. "Uniform Convergence Rates Of Kernel Estimators With Heterogeneous Dependent Data," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1433-1445, October.
    9. Westerhoff, Frank & Reitz, Stefan, 2005. "Commodity price dynamics and the nonlinear market impact of technical traders: empirical evidence for the US corn market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 641-648.
    10. Michael Creel, 2008. "Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments," UFAE and IAE Working Papers 725.08, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 02 Jun 2008.
    11. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data-Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    12. Fernández-Villaverde, Jesús & Rubio-Ramírez, Juan Francisco, 2010. "Macroeconomics and Volatility: Data, Models, and Estimation," CEPR Discussion Papers 8169, C.E.P.R. Discussion Papers.
    13. Torben G. Andersen & Luca Benzoni & Jesper Lund, 2002. "An Empirical Investigation of Continuous-Time Equity Return Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1239-1284, June.
    14. Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "A dynamic analysis of moving average rules," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1729-1753.
    15. Westerhoff Frank H. & Reitz Stefan, 2003. "Nonlinearities and Cyclical Behavior: The Role of Chartists and Fundamentalists," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-15, December.
    16. Reiner Franke & Frank Westerhoff, 2016. "Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 11(1), pages 1-34, April.
    17. 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.
    18. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2010. "Behavioral heterogeneity in the option market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2273-2287, November.
    19. 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.
    20. Ellen, Saskia ter & Zwinkels, Remco C.J., 2010. "Oil price dynamics: A behavioral finance approach with heterogeneous agents," Energy Economics, Elsevier, vol. 32(6), pages 1427-1434, November.
    21. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters,in: Theory Of Valuation, chapter 5, pages 129-164 World Scientific Publishing Co. Pte. Ltd..
    22. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    23. Goldbaum, David, 1999. "A nonparametric examination of market information: application to technical trading rules," Journal of Empirical Finance, Elsevier, vol. 6(1), pages 59-85, January.
    24. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    25. 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.
    26. Eelke de Jong & Willem Verschoor & Remco Zwinkels, 2009. "A heterogeneous route to the European monetary system crisis," Applied Economics Letters, Taylor & Francis Journals, vol. 16(9), pages 929-932.
    27. Hommes,Cars, 2015. "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems," Cambridge Books, Cambridge University Press, number 9781107564978.
    28. Jin-Chuan Duan & Jean-Guy Simonato, 1995. "Empirical Martingale Simulation for Asset Prices," CIRANO Working Papers 95s-43, CIRANO.
    29. Diks, Cees & van der Weide, Roy, 2005. "Herding, a-synchronous updating and heterogeneity in memory in a CBS," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 741-763, April.
    30. Cornea, A. & Hommes, C.H. & Massaro, D., 2012. "Behavioral Heterogeneity in U.S. Inflation Dynamics," CeNDEF Working Papers 12-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    31. Michael Creel & Dennis Kristensen, 2012. "Estimation of dynamic latent variable models using simulated non‐parametric moments," Econometrics Journal, Royal Economic Society, vol. 15(3), pages 490-515, October.
    32. Lui, Yu-Hon & Mole, David, 1998. "The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence," Journal of International Money and Finance, Elsevier, vol. 17(3), pages 535-545, June.
    33. repec:eee:dyncon:v:80:y:2017:i:c:p:101-124 is not listed on IDEAS
    34. Peter Winker & Manfred Gilli & Vahidin Jeleskovic, 2007. "An objective function for simulation based inference on exchange rate data," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 2(2), pages 125-145, December.
    35. Zhenxi, Chen & Lux, Thomas, 2015. "Estimation of sentiment effects in financial markets: A simulated method of moments approach," FinMaP-Working Papers 37, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    36. 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).
    37. 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.
    38. Wan, Jer-Yuh & Kao, Chung-Wei, 2009. "Evidence on the contrarian trading in foreign exchange markets," Economic Modelling, Elsevier, vol. 26(6), pages 1420-1431, November.
    39. Kukacka, Jiri & Barunik, Jozef, 2013. "Behavioural breaks in the heterogeneous agent model: The impact of herding, overconfidence, and market sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5920-5938.
    40. Carl Chiarella & Tony He, 2002. "An Adaptive Model on Asset Pricing and Wealth Dynamics with Heterogeneous Trading Strategies," Computing in Economics and Finance 2002 135, Society for Computational Economics.
    41. 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.
    42. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    43. Chiarella, Carl & He, Xue-Zhong & Zwinkels, Remco C.J., 2014. "Heterogeneous expectations in asset pricing: Empirical evidence from the S&P500," Journal of Economic Behavior & Organization, Elsevier, vol. 105(C), pages 1-16.
    44. Franke, Reiner, 2009. "Applying the method of simulated moments to estimate a small agent-based asset pricing model," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 804-815, December.
    45. Frankel, Jeffrey A & Froot, Kenneth A, 1990. "Chartists, Fundamentalists, and Trading in the Foreign Exchange Market," American Economic Review, American Economic Association, vol. 80(2), pages 181-185, May.
    46. 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.
    47. De Grauwe, Paul & Grimaldi, Marianna, 2006. "Exchange rate puzzles: A tale of switching attractors," European Economic Review, Elsevier, vol. 50(1), pages 1-33, January.
    48. Kouwenberg, Roy & Zwinkels, Remco, 2014. "Forecasting the US housing market," International Journal of Forecasting, Elsevier, vol. 30(3), pages 415-425.
    49. 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.
    50. Biondi, Yuri & Giannoccolo, Pierpaolo & Galam, Serge, 2012. "Formation of share market prices under heterogeneous beliefs and common knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5532-5545.
    51. Jozef Barunik & Lukas Vacha & Miloslav Vosvrda, 2009. "Smart predictors in the heterogeneous agent model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(2), pages 163-172, November.
    52. F. M. Bandi & J. R. Russell, 2008. "Microstructure Noise, Realized Variance, and Optimal Sampling," Review of Economic Studies, Oxford University Press, vol. 75(2), pages 339-369.
    53. 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.
    54. Bolt, W. & Demertzis, D. & Diks, C.G.H. & Van der Leij, M.J., 2011. "Complex Methods in Economics: An Example of Behavioral Heterogeneity in House Prices," CeNDEF Working Papers 11-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    55. Peter Winker and Manfred Gilli, 2001. "Indirect Estimation of the Parameters of Agent Based Models of Financial Markets," Computing in Economics and Finance 2001 59, Society for Computational Economics.
    56. Willem F.C. Verschoor & Remco C.J. Zwinkels, 2013. "Do foreign exchange fund managers behave like heterogeneous agents?," Quantitative Finance, Taylor & Francis Journals, vol. 13(7), pages 1125-1134, February.
    57. Vacha, Lukas & Barunik, Jozef & Vosvrda, Miloslav, 2012. "How do skilled traders change the structure of the market," International Review of Financial Analysis, Elsevier, vol. 23(C), pages 66-71.
    58. De Grauwe, Paul & Grimaldi, Marianna, 2005. "Heterogeneity of agents, transactions costs and the exchange rate," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 691-719, April.
    59. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, Oxford University Press, vol. 108(1), pages 137-156.
    60. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    61. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
    62. Lof, Matthijs, 2012. "Heterogeneity in stock prices: A STAR model with multivariate transition function," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1845-1854.
    63. 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.
    64. Chiarella, Carl & ter Ellen, Saskia & He, Xue-Zhong & Wu, Eliza, 2015. "Fear or fundamentals? Heterogeneous beliefs in the European sovereign CDS market," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 19-34.
    65. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    66. 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.
    67. Allen, Helen & Taylor, Mark P, 1990. "Charts, Noise and Fundamentals in the London Foreign Exchange Market," Economic Journal, Royal Economic Society, vol. 100(400), pages 49-59, Supplemen.
    68. 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.
    69. Barunik, J. & Vosvrda, M., 2009. "Can a stochastic cusp catastrophe model explain stock market crashes?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1824-1836, October.
    70. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
    71. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    72. Filippo Altissimo & Antonio Mele, 2009. "Simulated Non-Parametric Estimation of Dynamic Models," Review of Economic Studies, Oxford University Press, vol. 76(2), pages 413-450.
    73. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    74. Goldbaum, David & Zwinkels, Remco C.J., 2014. "An empirical examination of heterogeneity and switching in foreign exchange markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 667-684.
    75. Diks, Cees & Wang, Juanxi, 2016. "Can a stochastic cusp catastrophe model explain housing market crashes?," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 68-88.
    76. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    77. Jin-Chuan Duan & Jean-Guy Simonato, 1998. "Empirical Martingale Simulation for Asset Prices," Management Science, INFORMS, vol. 44(9), pages 1218-1233, September.
    78. Stefan Reitz & Ulf Slopek, 2009. "Non-Linear Oil Price Dynamics: A Tale of Heterogeneous Speculators?," German Economic Review, Verein für Socialpolitik, vol. 10, pages 270-283, August.
    79. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
    80. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    81. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    82. Blake LeBaron & Leigh Tesfatsion, 2008. "Modeling Macroeconomies as Open-Ended Dynamic Systems of Interacting Agents," American Economic Review, American Economic Association, vol. 98(2), pages 246-250, May.
    83. ter Ellen, Saskia & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2013. "Dynamic expectation formation in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 75-97.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Sciences Po publications 2017-09, Sciences Po.
    2. Grazzini, Jakob & Richiardi, Matteo G. & Tsionas, Mike, 2017. "Bayesian estimation of agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 26-47.
    3. 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).
    4. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    More about this item

    Keywords

    heterogeneous agent model; simulated maximum likelihood; estimation; intensity of choice; switching;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:fmpwps:63. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics). General contact details of provider: http://edirc.repec.org/data/vakiede.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.