Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T09:03:40.242Z Has data issue: false hasContentIssue false
  • English
  • Français

A DYNAMIC ANALYSIS OF THE MICROSTRUCTURE OF MOVING AVERAGE RULES IN A DOUBLE AUCTION MARKET

Published online by Cambridge University Press:  18 April 2011

Carl Chiarella ,
Xue-Zhong He  and
Paolo Pellizzari
Carl Chiarella
Affiliation:
University of Technology, Sydney
Xue-Zhong He
Affiliation:
University of Technology, Sydney
Paolo Pellizzari*
Affiliation:
Ca' Foscari University
*
Address correspondence to: Paolo Pellizzari, Dipartimento di Matematica Applicata, S. Giobbe—Cannaregio 873, 30121 Venice, Italy; e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Inspired by the theoretically oriented dynamic analysis of moving average rules in the model of Chiarella, He, and Hommes (CHH) [Journal of Economic Dynamics and Control 30 (2006), 1729—1753], this paper conducts a dynamic analysis of a more realistic microstructure model of continuous double auctions in which the probability of heterogeneous agents trading is determined by the rules of either fundamentalists mean-reverting to the fundamental or chartists choosing moving average rules based on their relative performance. With such a realistic market microstructure, the model is able not only to obtain the results of the CHH model but also to characterize most of the stylized facts including volatility clustering, insignificant autocorrelations (ACs) of returns, and significant slowly decaying ACs of the absolute returns. The results seem to suggest that a comprehensive explanation of several statistical properties of returns is possible in a framework where both behavioral traits and realistic microstructure have a role.

Keywords

Microstructure ModelContinuous Double AuctionsHeterogeneous AgentsStylized Facts
Type
Articles
Information
Macroeconomic Dynamics , Volume 16 , Issue 4 , September 2012 , pp. 556 - 575
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

Becker, R.A., Chambers, J.M., and Wilks, A.R. (1988) The New S Language: A Programming Environment for Data Analysis and Graphics. Pacific Grove, CA: Wadsworth and Brooks/Cole.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 and Control 22, 1235–1274.CrossRefGoogle Scholar
Chiarella, C., Dieci, R., and Gardini, L. (2002) Speculative behaviour and complex asset price dynamics. Journal of Economic Behavior and Organization 49, 173–197.CrossRefGoogle Scholar
Chiarella, C., Dieci, R., and Gardini, L. (2005) The dynamic interaction of speculation and diversification. Applied Mathematical Finance 12, 17–52.CrossRefGoogle Scholar
Chiarella, C., Dieci, R., and He, X. (2006a) Heterogeneous expectations and speculative behaviour in a dynamic multi-asset framework. Journal of Economic Behavior and Organization 62, 402–427.Google Scholar
Chiarella, C., Dieci, R., and He, X. (2009a) Heterogeneity, market mechanisms and asset price dynamics. In Hens, T. and Scheink-Hoppe, K. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 277–344. Amsterdam: North-Holland.CrossRefGoogle Scholar
Chiarella, C. and He, X. (2001) Asset price and wealth dynamics under heterogeneous expectations. Quantitative Finance 1, 509–526.CrossRefGoogle Scholar
Chiarella, C. and He, X. (2002) Heterogeneous beliefs, risk and learning in a simple asset pricing model. Computational Economics 19, 95–132.CrossRefGoogle Scholar
Chiarella, C. and He, X. (2003) Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker. Macroeconomic Dynamics 7, 503–536.CrossRefGoogle Scholar
Chiarella, C., He, X., and Hommes, C. (2006b) A dynamic analysis of moving average rules. Journal of Economic Dynamics and Control 30, 1729–1753.CrossRefGoogle Scholar
Chiarella, C., He, X., and Hommes, C. (2006c) Moving average rules as a source of market instability. Physica A 370, 12–17.CrossRefGoogle Scholar
Chiarella, C. and Iori, G. (2002) A simulation analysis of the microstructure of double auction markets. Quantitative Finance 2, 346–353.CrossRefGoogle Scholar
Chiarella, C., Iori, G., and Perellò, J. (2009b) The impact of heterogeneous trading rules on the limit order book and order flows. Journal of Economic Dynamics and Control 33, 525–537.CrossRefGoogle Scholar
DeGrauwe, P. and Grimaldi, M. (2006) Exchange rate puzzles. A tale of switching attractors. European Economic Review 50, 1–33.Google 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
Gode, D. and Sunder, S. (1993) Allocative efficiency of markets with zero intelligence traders. Journal of Political Economy 101, 119–137.CrossRefGoogle Scholar
He, X. and Li, Y. (2007) Power law behaviour, heterogeneity, and trend chasing. Journal of Economic Dynamics and Control 31, 3396–3426.CrossRefGoogle Scholar
He, X. and Li, Y. (2008) Heterogeneity, convergence and autocorrelations. Quantitative Finance 8, 58–79.CrossRefGoogle Scholar
Hommes, C. (2001) Financial markets as nonlinear adaptive evolutionary systems. Quantitative Finance 1, 149–167.CrossRefGoogle Scholar
Hommes, C. (2002) Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proceedings of the National Academy of Science of the United States of America 99, 7221–7228.CrossRefGoogle ScholarPubMed
Hommes, C. (2006) Heterogeneous agent models in economics and finance. In Tesfatsion, L. and Judd, K. (eds.), Handbook of Computational Economics, pp. 1109–1186. Amsterdam: North-Holland.Google Scholar
LeBaron, B. (2006) Agent-based computational finance. In Tesfatsion, L. and Judd, K. (eds.), Handbook of Computational Economics, pp. 1187–1233. Amsterdam: North-Holland.Google Scholar
LiCalzi, M. and Pellizzari, P. (2003) Fundamentalists clashing over the book: A study of order driven stock markets. Quantitative Finance 3, 470–480.CrossRefGoogle Scholar
Lux, T. (2006) Financial Power Laws: Empirical Evidence, Models, and Mechanism. Economics Working Papers n. 2006.12, Department of Economics, Christian-Albrechts-University of Kiel.Google Scholar
Lux, T. (2009) Stochastic behavioural asset pricing and stylized facts. In Hens, T. and Scheink-Hoppe, K. (eds.), Handbook of Financial Markets: Dynamics and Evolution, pp. 161–215. Amsterdam: North-Holland.CrossRefGoogle Scholar
Maslov, S. (2000) A simple model of an order-driven market. Physica A 278, 571–578.CrossRefGoogle Scholar
Pagan, A. (1996) The econometrics of financial markets. Journal of Empirical Finance 3, 15–102.CrossRefGoogle Scholar
Pellizzari, P. and Westerhoff, F. (2009) Some effects of transaction taxes under different microstructures. Journal of Economic Behavior and Organization 72, 850–863.CrossRefGoogle Scholar
Westerhoff, F. (2003) Speculative markets and the effectiveness of price limits. Journal of Economic Dynamics and Control 28, 439–508.CrossRefGoogle Scholar
Westerhoff, F. (2004) Multiasset market dynamics. Macroeconomic Dynamics 8, 591–616.CrossRefGoogle Scholar