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Efficient Calibration of a Financial Agent-Based Model Using the Method of Simulated Moments

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12744))

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Abstract

We propose a new efficient method of calibrating agent-based models using the Method of Simulated Moments. It utilizes the Filtered Neighborhoods optimization algorithm, gradually narrowing down the search area by examining local neighborhoods of promising solutions. The new method obtains better calibration accuracy for a benchmark financial agent-based model in comparison to a broad selection of other methods, while using just a tiny fraction of their computational budget.

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Notes

  1. 1.

    The notions of “calibration” and “validation” of HAMs do not have unambiguous definitions. Here they are used broadly, designating a process of arriving at HAM parameter values that provide the best fit of model output to some reference series.

  2. 2.

    Search ranges for all parameters need to be scaled in the same way first.

References

  1. Barde, S.: A practical, accurate, information criterion for Nth order Markov processes. Comput. Econ. 50(2), 281–324 (2016). https://doi.org/10.1007/s10614-016-9617-9

    Article  Google Scholar 

  2. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  3. Brochu, E., Cora, V.M., de Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. CoRR abs/1012.2599 (2010)

    Google Scholar 

  4. Brock, W.A., Hommes, C.H.: Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J. Econ. Dyn. Control 22, 1235–1274 (1998). https://doi.org/10.1016/S0165-1889(98)00011-6

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J.: Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(3), 680–691 (2009). https://doi.org/10.1109/TSMCA.2009.2012436

    Article  Google Scholar 

  6. Chen, S.H., Chang, C.L., Du, Y.R.: Agent-based economic models and econometrics. Knowl. Eng. Rev. 27(2), 187–219 (2012). https://doi.org/10.1017/S0269888912000136

    Article  Google Scholar 

  7. Chen, Z., Lux, T.: Estimation of sentiment effects in financial markets: a simulated method of moments approach. Comput. Econ. 52(3), 711–744 (2016). https://doi.org/10.1007/s10614-016-9638-4

    Article  Google Scholar 

  8. Dieci, R., He, X.Z.: Heterogeneous agent models in finance. In: Hommes, C., LeBaron, B. (eds.) Handbook of Computational Economics, vol. 4, chap. 5, pp. 257–328. Handbooks in Economics, Elsevier (2018). https://doi.org/10.1016/bs.hescom.2018.03.002

  9. Dutang, C., Savicky, P.: randtoolbox: Generating and Testing Random Numbers (2020). R package version 1.30.1

    Google Scholar 

  10. Ellen, S., Verschoor, W.F.C.: Heterogeneous beliefs and asset price dynamics: a survey of recent evidence. In: Jawadi, F. (ed.) Uncertainty, Expectations and Asset Price Dynamics. DMEEF, vol. 24, pp. 53–79. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98714-9_3

    Chapter  Google Scholar 

  11. Fagiolo, G., Guerini, M., Lamperti, F., Moneta, A., Roventini, A.: Validation of agent-based models in economics and finance. In: Beisbart, C., Saam, N.J. (eds.) Computer Simulation Validation. SFMA, pp. 763–787. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-70766-2_31

    Chapter  Google Scholar 

  12. Grazzini, J., Richiardi, M.G., Tsionas, M.: Bayesian estimation of agent-based models. J. Econ. Dyn. Control 77, 26–47 (2017). https://doi.org/10.1016/j.jedc.2017.01.014

    Article  MathSciNet  MATH  Google Scholar 

  13. He, X.Z.: Recent developments in asset pricing with heterogeneous beliefs and adaptive behaviour of financial markets. In: Bischi, G., Chiarella, C., Sushko, I. (eds.) Global Analysis of Dynamic Models in Economics and Finance: Essays in Honour of Laura Gardini, pp. 3–34. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-29503-4_1

  14. Hommes, C.H.: Heterogeneous agent models in economics and finance. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, chap. 23, pp. 1109–1186. Handbooks in Economics, Elsevier (2006). https://doi.org/10.1016/S1574-0021(05)02023-X

  15. Hong, H.S., Hickernell, F.J.: Algorithm 823: implementing scrambled digital sequences. ACM Trans. Math. Softw. 29(2), 95–109 (2003). https://doi.org/10.1145/779359.779360

    Article  MathSciNet  MATH  Google Scholar 

  16. Klein, A., Falkner, S., Bartels, S., Hennig, P., Hutter, F.: Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 54, pp. 528–536. PMLR, Fort Lauderdale (2017)

    Google Scholar 

  17. Knysh, P., Korkolis, Y.: Blackbox: a procedure for parallel optimization of expensive black-box functions. CoRR abs/1605.00998 (2016)

    Google Scholar 

  18. Kukacka, J., Barunik, J.: Estimation of financial agent-based models with simulated maximum likelihood. J. Econ. Dyn. Control 85, 21–45 (2017). https://doi.org/10.1016/j.jedc.2017.09.006

    Article  MathSciNet  MATH  Google Scholar 

  19. Lamperti, F.: An information theoretic criterion for empirical validation of simulation models. Econom. Stat. 5, 83–106 (2018). https://doi.org/10.1016/j.ecosta.2017.01.006

    Article  MathSciNet  Google Scholar 

  20. LeBaron, B.: Agent-based computational finance. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, chap. 24, pp. 1187–1233. Handbooks in Economics, Elsevier (2006). https://doi.org/10.1016/S1574-0021(05)02024-1

  21. Lux, T., Zwinkels, R.C.J.: Empirical validation of agent-based models. In: Hommes, C., LeBaron, B. (eds.) Handbook of Computational Economics, vol. 4, chap. 8, pp. 437–488. Handbooks in Economics, Elsevier (2018). https://doi.org/10.1016/bs.hescom.2018.02.003

  22. Platt, D.: A comparison of economic agent-based model calibration methods. J. Econ. Dyn. Control 113 (2020). https://doi.org/10.1016/j.jedc.2020.103859

  23. Rakshit, P., Konar, A., Das, S.: Swarm and evolutionary computation. J. Am. Stat. Assoc. 33, 18–45 (2017). https://doi.org/10.1016/j.swevo.2016.09.002

    Article  Google Scholar 

  24. Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148–175 (2016). https://doi.org/10.1109/JPROC.2015.2494218

    Article  Google Scholar 

  25. Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2951–2959. Curran Associates, Inc. (2012)

    Google Scholar 

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Authors and Affiliations

  1. Kozminski University, Warsaw, Poland

    Piotr Zegadło

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  1. Piotr Zegadło
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Correspondence to Piotr Zegadło .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Zegadło, P. (2021). Efficient Calibration of a Financial Agent-Based Model Using the Method of Simulated Moments. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-77967-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77966-5

  • Online ISBN: 978-3-030-77967-2

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