El uso de modelos computacionales (ABM) en economía y sus implicaciones éticas: el caso del análisis de la dinámica de variación de precios en un ambiente de complejidad financiera
Keywords:
Agent, Complexity, Finance, Price, VolatilityAbstract
The aim of this paper is to provide the reader with a better insight into the pricing dynamics of financial markets by adopting assumptions from complexity theory. The motivation is to make a formal but accessible presentation of the application of the Agent Base Model (ABM) in financial markets. The importance of including a combination of complexity theory and ABMs for economics from an ethical perspective is to recognize that the adoption of assumptions that are better adapted to the reality of economic processes and the verification of results are indispensable to obtain conclusions appropriate to these economic processes. On the other hand, the analysis of price variation in financial markets under assumptions of complexity theory is of utmost importance to understand their dynamics. On the one hand, the number of participants has a depth effect on price variation, but on the other hand, asset returns have an intensity effect, which are caused by the iteration of participants' expectations. Finally, the contribution of this paper seeks to expose how the analysis of only two factors that are inherent to financial markets can create an environment of financial complexity.
Metrics
References
Alfarano, S., Lux, T. y Wagner, F., 2011. Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach. Journal of Economic Dynamics and Control, 35(1), pp.136-149.
Axtell, R.L., 2016. 30 years of agent-based modeling in the social sciences: A review. Complexity, 21(S2), pp.15-20.
Aymanns, C. y Farmer, J.D., 2015. The dynamics of the leverage cycle. Journal of Economic Dynamics and Control, 50, pp.155-179.
Biondo, A.E., Pluchino, A., Rapisarda, A. y Helbing, D., 2013. Are random trading strategies more successful than technical ones? PLoS ONE, 8(7).
Boss, M., Elsinger, H., Summer, M. y Thurner, S., 2004. Network topology of the interbank market. Quantitative Finance, 4(6), pp.677-684.
Chiarella, C., He, X.Z. y Hommes, C., 2010. A dynamic analysis of moving average rules. Journal of Economic Dynamics and Control, 34(5), pp.935-948.
Chen, S.H. y Yeh, C.H., 2012. Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control, 36(4), pp.603-617.
Cristelli, M., Batty, M. y Pietronero, L., 2012. There is more than a power law in Zipf. Scientific Reports, 2(1), p.812.
Dembinski P. y Beretta, S. 2014. Beyond the Financial Crisis. Towards a Christian Perspective for Action, ed. The Caritas in Veritate Foundation Working Papers, Mathias Nebel and Joseph Godal, pp.11-97.
Farmer, J.D. y Foley, D., 2009. The economy needs agent-based modelling. Nature, 460(7256), pp.685-686.
Gatti, D.D., Desiderio, S., Gaffeo, E., Cirillo, P. y Gallegati, M., 2011. Macroeconomics from the bottom-up. Springer.
Gerding, E.H., Robu, V., Stein, S., Parkes, D.C., Rogers, A. y Jennings, N.R., 2011. Online mechanism design for electric vehicle charging. AAAI Conference on Artificial Intelligence, 26(1), pp.1343-1349.
Gomes, R. 2013. “La responsabilidad económica empresarial: una regulación ética para unos mercados financieros ordenados”, Finance and Common good Journal, No. 40-41, pp. 31-50
Hommes, C. y Wagener, F., 2010. Complex evolutionary systems in behavioral finance. Handbook of Behavioral Finance, pp.217-250.
Iori, G. y Porter, J., 2012. Agent-based modeling for financial markets. Handbook of Computational Economics, 4, pp.527-564.
Kirman, A., 2010. The economic crisis is a crisis for economic theory. CESifo Economic Studies, 56(4), pp.498-535.
LeBaron, B., 2011. Heterogeneous gain learning and the dynamics of asset prices. Journal of Economic Behavior & Organization, 78(3), pp.206-223.
LeBaron, B. y Tesfatsion, L., 2008. Modeling macroeconomies as open-ended dynamic systems of interacting agents. American Economic Review, 98(2), pp.246-250.
Lux, T., 2009. Applications of statistical physics in finance and economics. A New Order of Things, pp.213-258.
Lux, T. y Zwinkels, R.C., 2018. Empirical validation of agent-based models. Handbook of Computational Economics, 4, pp.437-488.
Mandel, A. and Schnabl, G., 2011. The role of risk management for systemic risk. Journal of Banking & Finance, 35(12), pp.3288-3301.
Marengo, L., Pasquali, C. y Valente, M., 2013. Financial constraints and endogenous technical change. Journal of Economic Dynamics and Control, 37(11), pp.2253-2273.
Mazzoli, M. y Montagna, L., 2015. A complex network approach to portfolio theory and risk management. Physica A: Statistical Mechanics and its Applications, 436, pp.188-203.
Napoletano, M., Gaffard, J.L. y Sapio, S., 2014. Agent-based models and economic policy. Revue de l'OFCE, (134), pp.69-103.
Naticchioni, P., Ricci, A. y Piroli, G., 2012. The evolution of social norms in agent-based models. Journal of Economic Interaction and Coordination, 7(2), pp.169-188.
Nguyen, T.T. y Tran, Q.B., 2012. Modeling the impact of environmental policies on economic growth and financial development: An agent-based approach. Environmental and Resource Economics, 53(3), pp.393-418.
Pluchino, A., Biondo, A.E. y Rapisarda, A., 2019. Talent vs luck: The role of randomness in success and failure. Advances in Complex Systems, 22(3-4).
Raberto, M., Cincotti, S., Focardi, S.M. y Marchesi, M., 2001. Agent-based simulation of a financial market. Physica A: Statistical Mechanics and its Applications, 299(1-2), pp.319-327.
Roncella, A. y Roncella, L. 2019. “Finance Needs “Bilinguals” Too", Finance and Common good Journal, No. 46-47, pp. 43-58.
Schredelseker, K. y Hauser, F. eds., 2008. Complexity and artificial markets. Springer Science & Business Media.
Sornette, D., 2014. Physics and financial economics (1776-2014): Puzzles, Ising and agent-based models. Reports on Progress in Physics, 77(6).
Thurner, S., Farmer, J.D. y Geanakoplos, J., 2012. Leverage causes fat tails and clustered volatility. Quantitative Finance, 12(5), pp.695-707.
Thurner, S., 2017. Quantitative agent-based modeling of socio-economic systems. Physics Reports, 876, pp.1-86.
Tiziana, D., Cincotti, S., Raberto, M. y Teglio, A., 2010. Macroprudential policies in an agent-based artificial economy. Journal of Economic Behavior & Organization, 75(3), pp.234-254.
Westerhoff, F.H. y Franke, R., 2012. Agent-based models for economic policy design: Introduction to the special issue. Journal of Economic Behavior & Organization, 83(3), pp.423-428.
Windrum, P., Fagiolo, G. y Moneta, A., 2007. Empirical validation of agent-based models: Alternatives and prospects. Journal of Artificial Societies and Social Simulation, 10(2), p.8.
Zhang, W.B., 2011. A synthetic economic model of wealth distribution, social status, and financial crisis. Journal of Economic Interaction and Coordination, 6(2), pp.181-204.
Zhou, W.X. y Sornette, D., 2009. A case study of speculative financial bubbles in the South African stock market 2003-2006. Physica A: Statistical Mechanics and its Applications, 388(6), pp.869-880.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
CC BY-NC-ND 4.0 Creative Commons Attribution-NonCommercial-NoDerivative 4.0 Internal licenses. This license allows you to share, copy, distribute and transmit the work for non-commercial purposes, providing attribution is made to the authors (but not in a way that suggests that he endorses you or your use of the work). In order to access detailed and updated information on the license, please visit: https://creativecommons.org/licenses/by-nc-sa/4.0/