Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T13:42:36.657Z Has data issue: false hasContentIssue false
  • English
  • Français

IS MORE MEMORY IN EVOLUTIONARY SELECTION (DE)STABILIZING?

Published online by Cambridge University Press:  17 February 2011

Cars Hommes ,
Tatiana Kiseleva ,
Yuri Kuznetsov  and
Miroslav Verbic
Cars Hommes*
Affiliation:
University of Amsterdam
Tatiana Kiseleva
Affiliation:
University of Amsterdam
Yuri Kuznetsov
Affiliation:
Utrecht University
Miroslav Verbic
Affiliation:
University of Ljubljana
*
Address correspondence to: Cars Hommes, Roetersstraat 11, 1018WB Amsterdam, The Netherlands; e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

We investigate the effects of memory on the stability of evolutionary selection dynamics based on a multinomial logit model in a simple asset pricing model with heterogeneous beliefs. Whether memory is stabilizing or destabilizing depends in general on three key factors: (1) whether or not the weights on past observations are normalized; (2) the ecology or composition of forecasting rules, in particular the average trend extrapolation factor and the spread or diversity in biased forecasts; and (3) whether or not costs for information gathering of economic fundamentals have to be incurred.

Keywords

Fitness MeasureAsset PricingBifurcationsEvolutionary SelectionHeterogeneous BeliefsMemory Strength
Type
Articles
Information
Macroeconomic Dynamics , Volume 16 , Issue 3 , June 2012 , pp. 335 - 357
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alfarano, S., Lux, T., and Wagner, F. (2005) Estimation of agent-based models: The case of an asymmetric herding model. Computational Economics 26 (1), 19–49.CrossRefGoogle Scholar
Anufriev, M. (2008) Wealth driven competition in a speculative financial market: examples with maximizing agents. Quantitative Finance 8, 363–380.CrossRefGoogle Scholar
Anufriev, M., Assenza, T., Hommes, C., and Massaro, D. (2009) Interest Rate Rules and Macroeconomic Stability under Heterogeneous Expectations. CeNDEF working paper, University of Amsterdam.CrossRefGoogle Scholar
Anufriev, M. and Bottazzi, G. (2006) Equilibria, stability and asymptotic dominace in a speculative market with heterogeneous agents. Journal of Economic Dynamics and Control 30, 1787–1835.CrossRefGoogle Scholar
Anufriev, M. and Hommes, C.H. (in press) Evolution of market heuristics. Knowledge Engineering Review.Google Scholar
Boswijk, H.P., Hommes, C.H., and Manzan, S. (2007) Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control 31, 1938–1970.CrossRefGoogle Scholar
Brock, W.A. and Durlauf, S.N. (2001) Discrete choice with social interactions. Review of Economic Studies 68 (2); 235–260.CrossRefGoogle Scholar
Brock, W.A. and Hommes, C.H. (1997) A rational route to randomness. Econometrica 65 (5); 1059–1095.CrossRefGoogle Scholar
Brock, W.A. and Hommes, C.H. (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22 (8–9), 1235–1274.CrossRefGoogle Scholar
Brock, W.A. and Hommes, C.H. (1999) Rational animal spirits. In Herings, P.J.J., van der Laan, G., and Talman, A.J.J. (eds.), The Theory of Markets, pp. 109–137. North-Holland: Amsterdam.Google Scholar
Brock, W.A., Hommes, C.H., and Wagener, F.O.O. (2005) Evolutionary dynamics in markets with many trader types. Journal of Mathematical Economics 41 (1–2): 7–42.CrossRefGoogle Scholar
Bullard, J., Evans, G.W., and Honkapohja, S. (2008) Monetary policy, judgment, and near-rational exuberance. American Economic Review 98 (3), 1163–1177.CrossRefGoogle Scholar
Camerer, C.F. (2003) Behavioral Game Theory: Experiments in Strategic Interaction. Princeton, NJ: Princeton University Press.Google Scholar
Camerer, C.F. and Ho, T.H. (1999) Experience-weighted attraction learning in normal form games. Econometrica 67, 827–874.CrossRefGoogle Scholar
Chiarella, C., Dieci, R., and He, X.-Z. (2009) Heterogeneity, market mechanisms, and asset price dynamics. In Hens, T. and Schenk-Hopé, K. R. (eds.), Handbook of Financial Markets: Dynamics and Evolution. Amsterdam: North-Holland.Google Scholar
Chiarella, C. and He, X.-Z. (2002) Heterogeneous beliefs, risk and learning in a simple asset pricing model. Computational Economics 19 (1), 95–132.CrossRefGoogle Scholar
Chiarella, C., He, X.-Z., and Hommes, C.H. (2006) A dynamic analysis of moving average rules. Journal of Economic Dynamics and Control 30 (9–10), 1729–1753.CrossRefGoogle Scholar
Chiarella, C., He, X.-Z., and Zhu, P. (2003) Fading Memory Learning in the Cobweb Model with Risk Averse Heterogeneous Producers. Research Paper Series No. 108, Quantitative Finance Research Centre, University of Technology, Sydney.Google Scholar
Da Silva, S. (2001) Chaotic exchange rate dynamics redux. Open Economies Review 12 (3), 281–304.CrossRefGoogle Scholar
De Grauwe, P., Dewachter, H., and Embrechts, M. (1993) Exchange Rate Theories: Chaotic Models of the Foreign Exchange Markets. Oxford, UK: Blackwell.Google Scholar
De Grauwe, P. and Grimaldi, M. (2005) Heterogeneity of agents, transaction costs and the exchange rate. Journal of Economic Dynamics and Control 29 (4), 691–719.CrossRefGoogle Scholar
De Grauwe, P. and Grimaldi, M. (2006) Exchange rate puzzles: A tale of switching attractors. European Economic Review 50 (1), 1–33.CrossRefGoogle Scholar
Diks, C.G.H. and van der Weide, R. (2005) Herding, a-synchronous updating and heterogeneity in memory in a CBS. Journal of Economic Dynamics and Control 29 (4), 741–763.CrossRefGoogle Scholar
Duffy, J. (1994) On learning and the nonuniqueness of equilibrium in an overlapping generations model with fiat money. Journal of Economic Theory 64 (2), 541–553.CrossRefGoogle Scholar
Evans, G.W. and Honkapohja, S. (2001) Learning and Expectations in Macroeconomics. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Evans, G.W. and Honkapohja, S. (2003) Adaptive learning and monetary policy design. Journal of Money, Credit and Banking 35 (6), 1045–1072.CrossRefGoogle Scholar
Evans, G.W. and McGough, B. (2005) Monetary policy, indeterminacy and learning. Journal of Economic Dynamics and Control 29 (11), 1809–1840.CrossRefGoogle Scholar
Evstigneev, I., Hens, T., and Schenk-Hoppé, K.R. (2009) Evolutionary finance. In Hens, T. and Schenk-Hoppé, K.R. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 507–566. Amsterdam: North-Holland.CrossRefGoogle Scholar
Friedman, M. (1953) The case of flexible exchange rates. In Essays in Positive Economics. Chicago: University of Chicago Press.Google Scholar
Gaunersdorfer, A. (2000) Endogenous fluctuations in a simple asset pricing model with heterogeneous agents. Journal of Economic Dynamics and Control 24 (5–7), 799–831.CrossRefGoogle Scholar
Gaunersdorfer, A., Hommes, C.H., and Wagener, F.O.O. (2008) Bifurcation routes to volatility clustering under evolutionary learning. Journal of Economic Behavior and Organization 67 (1), 27–47.CrossRefGoogle Scholar
Goldbaum, D. and Mizrach, B. (2008) Estimating the intensity of choice in a dynamic mutual fund allocation decision Journal of Economic Dynamics and Control 32 (12), 3866–3876.CrossRefGoogle Scholar
Hommes, C.H. (2006) Heterogeneous agent models in economics and finance. In Tesfatsion, L. and Judd, K.L. (eds.), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics, pp. 1109–1186. Amsterdam: Elsevier Science.CrossRefGoogle Scholar
Hommes, C.H., Sonnemans, J., Tuinstra, J., and van de Velden, H. (2005) Coordination of expectations in asset pricing experiments. Review of Financial Studies 18, 955–980.CrossRefGoogle Scholar
Hommes, C.H. and Wagener, F.O.O. (2009) Complex evolutionary systems in behavioral finance. In Hens, T. and Schenk-Hoppé, K. R. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 217–276. Amsterdam: North-Holland.CrossRefGoogle Scholar
Honkapohja, S. and Mitra, K. (2003) Learning with bounded memory in stochastic models. Journal of Economic Dynamics and Control 27 (8), 1437–1457.CrossRefGoogle Scholar
Kuznetsov, Yu. A. (2004) Elements of Applied Bifurcation Theory, 3rd ed.New York: Springer.CrossRefGoogle Scholar
LeBaron, B. (2000) Agent-based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control, 24 (5–7), 679–702.CrossRefGoogle Scholar
LeBaron, B. (2001) Evolution and time horizons in an agent-based stock market. Macroeconomic Dynamics 5, 225–254.CrossRefGoogle Scholar
LeBaron, B. (2002) Short-memory traders and their impact on group learning in financial markets. Proceedings of the National Academy of Sciences of the United States of America 99(10/3), 7201–7206.CrossRefGoogle Scholar
LeBaron, B. (2006) Agent-based computational finance. In Tesfatsion, L. and Judd, K.J. (eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, pp. 1187–1232. Amsterdam: Elsevier.CrossRefGoogle Scholar
Levy, M., Levy, H., and Solomon, S. (1994) A microscopic model of the stock market: Cycles, booms and crashes. Economics Letters 45, 103–111.CrossRefGoogle Scholar
Lux, T. (1995) Herd behavior, bubbles and crashes. Economic Journal 105, 881–896.CrossRefGoogle Scholar
Lux, T. (2009) Stochastic behavioral asset pricing models and the stylized facts. In Hens, T. and Schenk-Hoppé, K.R. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 161–215. Amsterdam: North-Holland.CrossRefGoogle Scholar
Lux, T. and Marchesi, M. (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature, 397 (6719), 498–500.CrossRefGoogle Scholar
Tuinstra, J. (2003) Beliefs equilibria in an overlapping generations model. Journal of Economic Behavior and Organization 50 (2), 145–164.CrossRefGoogle Scholar
Tuinstra, J. and Wagener, F.O.O. (2007) On learning Equilibria. Economic Theory 30 (3), 493–513.CrossRefGoogle Scholar