Computational Economics

, Volume 38, Issue 1, pp 53–83

Estimation of a Structural Stochastic Volatility Model of Asset Pricing



The paper estimates an elementary agent-based financial market model recently put forward by the same authors. Invoking the two trader types of fundamentalists and chartists, it comprises four features: price determination by excess demand; a herding mechanism that gives rise to a macroscopic adjustment equation for the population shares of the two groups; a rush towards fundamentalism when the price misalignment becomes too large; and, finally, differently strong noise components in the demand per chartist and fundamentalist trader, which implies a structural stochastic volatility in the returns. The estimation is performed using the method of simulated moments. Combining it with bootstrap and Monte Carlo methods, it is found that the model cannot be rejected by the empirical daily returns from a stock market index and a foreign exchange rate. Measures of the matching of the single moments are satisfactory, too, while the behavioural parameters are well identified and are able to discriminate between the two markets.


Stochastic volatility Method of simulated moments Daily returns Autocorrelation patterns Fundamentalist and technical trading 

JEL Classification

D84 G12 G14 G15 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alfarano, S., & Franke, R. (2007). A simple asymmetric herding model to distinguish between stock and foreign exchange markets. Working Paper. University of Kiel.
  2. Carrasco M., Florens J.-P. (2002) Simulation-based method of moments and efficiency. Journal of Business and Economic Statistics 20: 482–492CrossRefGoogle Scholar
  3. Chen, S.-H., Chang, C.-L., & Du, Y.-R. (2008). Agent-based economic models and econometrics. Paper presented at the Econophysics Colloquium, Kiel, August 2008.
  4. Cont R. (2001) Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance 1: 223–236CrossRefGoogle Scholar
  5. Davidson R., MacKinnon J. G. (2004) Econometric theory and methods. Oxford University Press, OxfordGoogle Scholar
  6. Farmer J. D., Joshi S. (2002) The price dynamics of common trading strategies. Journal of Economic Behavior and Organization 49: 149–171CrossRefGoogle Scholar
  7. Franke R. (2009) Applying the method of simulated moments to estimate a small agent-based asset pricing model. Journal of Empirical Finance 16: 804–815CrossRefGoogle Scholar
  8. Franke R. (2010) On the specification of noise in two agent-based asset pricing models. Journal of Economic Dynamics and Control 34: 1140–1152CrossRefGoogle Scholar
  9. Franke, R., & Westerhoff, F. (2009). Validation of a structural stochastic volatility model of asset pricing. Working Paper. Universities of Kiel and Bamberg.
  10. Gilli M., Winker P. (2003) A global optimization heuristic for estimating agent based models. Computational Statistics and Data Analysis 42: 299–312CrossRefGoogle Scholar
  11. Hasbrouck J. (2007) Empirical market microstructure. Oxford University Press, OxfordGoogle Scholar
  12. LeBaron B. (2006) Agent-based computational finance. In: Tesfatsion L., Judd K. (eds) Handbook of computational economics: Agent-based computational economics. North-Holland, Amsterdam, pp 1187–1233Google Scholar
  13. Lee B.-S., Ingram B. F. (1991) Simulation estimation of time series models. Journal of Econometrics 47: 197–205CrossRefGoogle Scholar
  14. Lux T. (2009) Stochastic behavioural asset-pricing models and the stylized facts. In: Hens T., Schenk-Hoppé K. R. (eds) Handbook of financial markets: Dynamics and evolution. North-Holland, Amsterdam, pp 161–216CrossRefGoogle Scholar
  15. Lux T., Ausloos M. (2002) Market fluctuations. I. Scaling, multiscaling, and their possible origins. In: Bunde A., Kropp J., Schellnhuber H. (eds) Science of disaster: Climate disruptions, heart attacks, and market crashes. Springer, Berlin, pp 373–410Google Scholar
  16. Lyons R. (2001) The microstructure approach to exchange rates. MIT Press, Cambridge, MAGoogle Scholar
  17. Manzan S., Westerhoff F. (2005) Representativeness of news and exchange rate dynamics. Journal of Economic Dynamics and Control 29: 677–689CrossRefGoogle Scholar
  18. Menkhoff L., Rebitzky R. R., Schröder M. (2009) Heterogeneity in exchange rate expectations: Evidence on the chartist–fundamentalist approach. Journal of Economic Behavior and Organization 70: 241–252CrossRefGoogle Scholar
  19. Press W. H. et al (1986) Numerical recipes: The art of scientific computing. Cambridge University Press, Cambridge, UKGoogle Scholar
  20. Shephard N. (2005) Stochastic volatility: Selected readings. Oxford University Press, OxfordGoogle Scholar
  21. Singleton K. J. (2006) Empirical dynamic asset pricing. Princeton University Press, PrincetonGoogle Scholar
  22. Taylor S. (2005) Asset price dynamics, volatility, and prediction. Princeton University Press, PrincetonGoogle Scholar
  23. Westerhoff F. (2008) The use of agent-based financial market models to test the effectiveness of regulatory policies. Jahrbücher für Nationalökonomie und Statistik (Journal of Economics and Statistics) 228: 195–227Google Scholar
  24. Winker P., Gilli M., Jeleskovic V. (2007) An objective function for simulation based inference on exchange rate data. Journal of Economic Interaction and Coordination 2: 125–145CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  1. 1.University of KielKielGermany
  2. 2.University of BambergBambergGermany

Personalised recommendations