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HETEROGENEOUS EXPECTATIONS AND ASSET PRICE DYNAMICS

Published online by Cambridge University Press:  10 February 2020

Noemi Schmitt
Noemi Schmitt*
Affiliation:
University of Bamberg
*
Address correspondence to: Noemi Schmitt, Department of Economics, University of Bamberg, Feldkirchenstrasse 21, 96045 Bamberg, Germany. e-mail: noemi.schmitt@uni-bamberg.de. Phone: +49 951 8632748.
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Abstract

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Within the seminal asset-pricing model by Brock and Hommes (Journal of Economic Dynamics Control 22, 1235–1274, 1998), heterogeneous boundedly rational agents choose between a fixed number of expectation rules to forecast asset prices. However, agents’ heterogeneity is limited in the sense that they typically switch between a representative technical and a representative fundamental expectation rule. Here, we generalize their framework by considering that all agents follow their own time-varying technical and fundamental expectation rules. Estimating our model using the method of simulated moments reveals that it is able to explain the statistical properties of the daily and monthly behavior of the S&P500 quite well. Moreover, our analysis reveals that heterogeneity is not only a realistic model property but clearly helps to explain the intricate dynamics of financial markets.

Keywords

Financial MarketsStylized FactsAgent-Based ModelsHeterogeneity and Coordination

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 6 , September 2021, pp. 1538 - 1568
Copyright
© Cambridge University Press 2020

Footnotes

Presented at the 25th International Conference on Computing in Economics and Finance (CEF), June 28–June 30, 2019, Ottawa, Canada, and at the 23rd Annual Workshop on Economic Science with Heterogeneous Interacting Agents (WEHIA), June 28–July 2, 2018, Tokyo, Japan. We thank Frank Westerhoff, Christian Proaño, Roberto Dieci, and Remco Zwinkels for their valuable feedback. The paper also benefited from constructive comments of two anonymous referees and an associate editor.

References

REFERENCES

Anufriev, M. and Hommes, C. (2012) Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. American Economic Journal: Microeconomics 4, 35–64.Google Scholar
Anufriev, M. and Tuinstra, J. (2013) The impact of short-selling constraints on financial market stability in a heterogeneous agents model. Journal of Economic Dynamics and Control 37, 1523–1543.CrossRefGoogle Scholar
Boswijk, H. P., Hommes, C. and Manzan, S. (2007) Behavioral heterogeneity in stock prices. Journal of Economic Dynamics and Control 31, 1938–1970.CrossRefGoogle Scholar
Brock, W. (1997) Asset price behavior in complex environments. In: Brian Arthur, W., Durlauf, S. N. and Lane, D. A. (eds.), The Economy as an Evolving Complex System II, pp. 385–423. Reading: Addison-Wesley.Google Scholar
Brock, W. and Hommes, C. (1997) A rational route to randomness. Econometrica 65, 1059–1095.CrossRefGoogle Scholar
Brock, W. and Hommes, C. (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics Control 22, 1235–1274.10.1016/S0165-1889(98)00011-6CrossRefGoogle Scholar
Brock, W., Hommes, C. and Wagener, F. (2005) Evolutionary dynamics in markets with many trader types. Journal of Mathematical Economics 41, 7–42.CrossRefGoogle Scholar
Brock, W., Hommes, C. and Wagener, F. (2009) More hedging instruments may destabilize markets. Journal of Economic Dynamics and Control 33, 1912–1928.CrossRefGoogle Scholar
Chiarella, C., Dieci, R. and He, X.-Z. (2009) Heterogeneity, market mechanisms, and asset price dynamics. In: Hens, T. and Schenk-Hoppé, K. R. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 277–344. Amsterdam: North-Holland.CrossRefGoogle Scholar
Cont, R. (2001) Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance 1, 223–236.10.1080/713665670CrossRefGoogle Scholar
Cont, R. and Bouchaud, J.-P. (2000) Herd behaviour and aggregate fluctuations in financial markets. Macroeconomic Dynamics 4, 170–196.CrossRefGoogle Scholar
Day, R. H. and Huang, W. (1990) Bulls, bears and market sheep. Journal of Economic Behavior and Organization 14, 299–329.CrossRefGoogle Scholar
De Grauwe, P. and Grimaldi, M. (2006) Exchange rate puzzles: A tale of switching attractors. European Economic Review 50, 1–33.CrossRefGoogle Scholar
Diks, C. and van der Weide, R. (2003) Heterogeneity as a natural source of randomness. Tinbergen Institute Discussion Paper No. 2003-073/1. Available at SSRN: https://ssrn.com/abstract=452381.CrossRefGoogle Scholar
Diks, C. and van der Weide, R. (2005) Herding, a-synchronous updating and heterogeneity in memory in a CBS. Journal of Economic Dynamics and Control 29, 741–763.CrossRefGoogle Scholar
Franke, R. (2009) Applying the method of simulated moments to estimate a small agent-based asset pricing model. Journal of Empirical Finance 16, 804–815.CrossRefGoogle Scholar
Franke, R. (2010) On the specification of noise in two agent-based asset pricing models. Journal of Economic Dynamics Control 34, 1140–1152.CrossRefGoogle Scholar
Franke, R. and Westerhoff, F. (2012) Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control 36, 1193–1211.CrossRefGoogle Scholar
Franke, R. and Westerhoff, F. (2016) Why a simple herding model may generate the stylized facts of daily returns: Explanation and estimation. Journal of Economic Interaction and Coordination 11, 1–34.CrossRefGoogle Scholar
Gaunersdorfer, A. (2000) Endogenous fluctuations in a simple asset pricing model with heterogeneous agents. Journal of Economic Dynamics and Control 24, 799–831.CrossRefGoogle Scholar
Gaunersdorfer, A., Hommes, C. and Wagener, F. (2008) Bifurcation routes to volatility clustering under evolutionary learning. Journal of Economic Behavior and Organization 67, 27–47.CrossRefGoogle Scholar
Gilli, M. and Winker, P. (2003) A global optimization heuristic for estimating agent based models. Computational Statistics and Data Analysis 42, 299–312.CrossRefGoogle Scholar
Graham, B. and Dodd, D. L. (1951) Security Analysis. New York: McGraw-Hill.Google Scholar
Hill, B. M. (1975) A simple general approach to inference about the tail of a distribution. Annals of Statistics 3, 1163–1174.CrossRefGoogle Scholar
Hommes, C. (2011) The heterogeneous expectations hypothesis: Some evidence from the lab. Journal of Economic Dynamics and Control 35, 1–24.CrossRefGoogle Scholar
Hommes, C. (2013) Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Hommes, C. and in ’t Veld, D. (2017) Booms, busts and behavioural heterogeneity in stock prices. Journal of Economic Dynamics and Control 80, 101–124.CrossRefGoogle Scholar
Hommes, C., 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., Sonnemans, J., Tuinstra, J. and van de Velden, H. (2008) Expectations and bubbles in asset pricing experiments. Journal of Economic Behavior and Organization 67, 116–133.CrossRefGoogle Scholar
Ito, T. (1990) Foreign exchange rate expectations: Micro survey data. The American Economic Review 80, 434–449.Google Scholar
Jongen, R., Verschoor, W. F.C., Wolff, C. C. P. and Zwinkels, R. C. J. (2012) Explaining dispersion in foreign exchange expectations: A heterogeneous agent approach. Journal of Economic Dynamics and Control 36, 719–735.CrossRefGoogle Scholar
Keynes, J. M. (1936) The General Theory of Employment, Interest, and Money. New York: Harcourt, Brace and Company.Google Scholar
Kirman, A. (1993) Ants, rationality, and recruitment. Quarterly Journal of Economics 108, 137–156.CrossRefGoogle Scholar
LeBaron, B. (2006) Agent-based computational finance. In: Tesfatsion, L. and Judd, K. L. (eds.), Handbook of Computational Economics: Agent-Based Computational Economics, pp. 1187–1233. Amsterdam: North-Holland.Google Scholar
LeBaron, B., Brian Arthur, W. and Palmer, R. (1999) Time series properties of an artificial stock market. Journal of Economic Dynamics and Control 23, 1487–1516.CrossRefGoogle Scholar
Lux, T. (1995) Herd behaviour, bubbles and crashes. The Economic Journal 105, 881–896.CrossRefGoogle Scholar
Lux, T. (2009) Stochastic behavioural asset-pricing models and the stylized facts. In: Hens, T. and Schenk-Hoppé, K. R. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 161–216. Amsterdam: North-Holland.CrossRefGoogle Scholar
Lux, T. and Ausloos, M. (2002) Market fluctuations I: Scaling, multiscaling, and their possible origins. In: Bunde, A., Kropp, J. and Schellnhuber, H. J. (eds.), Science of Disaster: Climate Disruptions, Heart Attacks, and Market Crashes, pp. 373–410. Berlin: Springer.Google Scholar
MacDonald, R. and Marsh, I. W. (1996) Currency forecasters are heterogeneous: Confirmation and consequences. Journal of International Money and Finance 5, 665–685.CrossRefGoogle Scholar
Mantegna, R. N. and Stanley, E. (2000) An Introduction to Econophysics. Cambridge: Cambridge University Press.Google Scholar
Manski, C. F. and McFadden, D. (1981) Structural Analysis of Discrete Data with Econometric Applications. Cambridge: MIT Press.Google Scholar
Menkhoff, L. and Taylor, M. P. (2007) The obstinate passion of foreign exchange professionals: technical analysis. Journal of Economic Literature 45, 936–972.CrossRefGoogle Scholar
Menkhoff, L., Rebitzky, R. R. and Schröder, M. (2009) Heterogeneity in exchange rate expectations: evidence on the chartist-fundamentalist approach. Journal of Economic Behavior and Organization 70, 241–252.CrossRefGoogle Scholar
Murphy, J. J. (1999) Technical Analysis of Financial Markets. New York: New York Institute of Finance.Google Scholar
Schmitt, N. and Westerhoff, F. (2017) Heterogeneity, spontaneous coordination and extreme events within large-scale and small-scale agent-based financial market models. Journal of Evolutionary Economics 5, 1041–1070.CrossRefGoogle Scholar
Shiller, R. J. (2015) Irrational Exuberance. Princeton: Princeton University Press.CrossRefGoogle Scholar
Ter Ellen, S., Verschoor, W. F. C. and Zwinkels, R. C. J. (2013) Dynamic expectation formation in the foreign exchange market. Journal of International Money and Finance 37, 75–97.CrossRefGoogle Scholar
Trueman, B. (1994) Analysts forecasts and herding behavior. Review of Financial Studies 7, 97–124.CrossRefGoogle Scholar
Welch, I. (2000) Herding among security analysts. Journal of Financial Economics 58, 369–396.CrossRefGoogle Scholar
Westerhoff, F. and Dieci, R. (2006) The effectiveness of Keynes–Tobin transaction taxes when heterogeneous agents can trade in different markets: a behavioral finance approach. Journal of Economic Dynamics Control 30, 293–322.CrossRefGoogle Scholar
Westerhoff, F. and Franke, R. (2012) Converse trading strategies, intrinsic noise and the stylized facts of financial markets. Quantitative Finance 12, 425–436.CrossRefGoogle Scholar
Winker, P., Gilli, M. and Jeleskovic, V. (2007) An objective function for simulation based inference on exchange rate data. Journal of Economic Interaction and Coordination 2, 125–145.CrossRefGoogle Scholar