Estimation of a Structural Stochastic Volatility Model of Asset Pricing
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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.
KeywordsStochastic volatility Method of simulated moments Daily returns Autocorrelation patterns Fundamentalist and technical trading
JEL ClassificationD84 G12 G14 G15
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- Alfarano, S., & Franke, R. (2007). A simple asymmetric herding model to distinguish between stock and foreign exchange markets. Working Paper. University of Kiel. http://www.bwl.uni-kiel.de/gwif/downloads_papers.php?lang=en.
- 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. http://www.bwl.uni-kiel.de/gwif/econophysics/abstracts/chen.pdf.
- Davidson R., MacKinnon J. G. (2004) Econometric theory and methods. Oxford University Press, OxfordGoogle Scholar
- Franke, R., & Westerhoff, F. (2009). Validation of a structural stochastic volatility model of asset pricing. Working Paper. Universities of Kiel and Bamberg. http://www.bwl.uni-kiel.de/gwif/downloads_papers.php?lang=en.
- Hasbrouck J. (2007) Empirical market microstructure. Oxford University Press, OxfordGoogle Scholar
- 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
- 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
- Lyons R. (2001) The microstructure approach to exchange rates. MIT Press, Cambridge, MAGoogle Scholar
- Press W. H. et al (1986) Numerical recipes: The art of scientific computing. Cambridge University Press, Cambridge, UKGoogle Scholar
- Shephard N. (2005) Stochastic volatility: Selected readings. Oxford University Press, OxfordGoogle Scholar
- Singleton K. J. (2006) Empirical dynamic asset pricing. Princeton University Press, PrincetonGoogle Scholar
- Taylor S. (2005) Asset price dynamics, volatility, and prediction. Princeton University Press, PrincetonGoogle Scholar
- 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