Alfarano, Simone and Franke, Reiner (2007) A simple asymmetric herding model to distinguish between stock and foreign exchange markets. Working Paper. Coventry: Warwick Business School, Financial Econometrics Research Centre. (Working papers (Warwick Business School. Financial Econometrics Research Centre)).

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Abstract

Drawing on previous work of one of the authors, the paper takes an asymmetric variant of Kirman’s ant model and combines it with an elementary asset pricing mechanism. The closed-form solution of the equilibrium probability distribution allows the specification of a tractable likelihood function for daily returns, which is then employed to estimate the model’s behavioural parameters for a large pool of Japanese stocks. By way of Monte Carlo simulations it is found that most of these markets belong to the same class, which is characterized by a dominance of the stylized noise traders. In contrast, the model assigns a number of major foreign exchange markets to a different class, where on average the majority of agents follows the fundamentalist trading rule. Implications for the tail index are also worked out.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	H Social Sciences > HG Finance
Divisions:	Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Stock markets -- Mathematical models, Probabilities, Foreign exchange rates -- Mathematical models, Monte Carlo method
Series Name:	Working papers (Warwick Business School. Financial Econometrics Research Centre)
Publisher:	Warwick Business School, Financial Econometrics Research Centre
Place of Publication:	Coventry
Date:	May 2007
Number:	No.07-
Number of Pages:	39
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Sixth Framework Programme (European Commission) (FP6)
Grant number:	516446 (SFP)
References:	Alfarano, S. (2006), An Agent-Based Stochastic Volatility Model. Dissertation, University of Kiel. Alfarano, S., Lux, T. and Wagner, W. (2007), “Time-variation of higher moments in a financial market with heterogeneous agents: An analytical approach”, Journal of Economic Dynamics and Control (forthcoming). Alfarano, S., Lux, T. and Wagner, W. (2006), “Empirical validation of stochastic models of interacting agents: A ‘maximally skewed’ noise trader model”, European Physical Journal B, 55, 183–187. Alfarano, S., Lux, T. and Wagner, W. (2005a), “Estimation of agent-based models: The case of an asymmetric herding model”, Computational Economics, 26, 19–49. Alfarano, S., Lux, T. and Wagner, W. (2005b), “Excess volatility and herding in an artificial financial market: Analytical approach and estimation”, in W. Franz et al. (eds.), Funktionsfähigkeit und Stabilität von Finanzmärkten. Tübingen: Mohr Siebeck; pp. 241–254. Boswijk, H.P., Hommes, C.H. and Manzan, S. (2006), “Behavioral heterogeneity in stock prices”, Journal of Economic Dynamics and Control (forthcoming). Davidson, R. and MacKinnon, J.G. (2004), Econometric Theory and Methods. New York: Oxford University Press. Frankel, J. and Froot, K. (1986), “Understanding the U.S. Dollar in the eighties: The expectations of chartists and fundamentalists”, Economic Record, 24, 24–38. Genon-Catalot, V., Jeantheau, T. and Laredo, C. (1999), “Parameter estimation for discretely observed stochastic volatility models”, Bernoulli, 5, 855–872. Gilli, M. and Winker, P. (2003), “A global optimization heuristic for estimating agent based models”, Computational Statistics and Data Analysis, 42, 299–312. Gopikrishnan, P., Meyer, M., Amaral, L.A.N. and Stanley, H.E. (1998), “Inverse cubic law for the distribution of stock price variations”, European Physical Journal B, 42, 299–312. Hill, B.M. (1975), “A simple general approach to inference about the tail of a distribution”, Annals of Statistics, 3, 1163–73. Kirman, A. (1993), “Ants, rationality, and recruitment”, Quarterly Journal of Economics, 108, 137–156. Kirman, A. (1991), “Epidemics of opinion and speculative bubbles in financial markets”, in M.P.Taylor (ed.), Money and Finanical Markets. Cambridge: Blackwell; pp. 354–368. LeBaron, B. (2001), “Stochastic volatility as a simple generator of apparent financial power laws and long memory”, Quantitative Finance, 1, 621–631. Lux, T. (2001), “Power laws and long memory”, Quantitative Finance, 1, 560–562. Pagan, A. (2001), “The econometrics of financial markets”, Journal of Empirical Finance, 3, 15–102. Vigfusson, R. (1997), “Switching between chartists and fundamentalists: A Markov regime-switching approach”, International Journal of Finance and Economics, 2, 291–305. Westerhoff, F. and Reitz, S. (2005), “Commodity price dynamics and the nonlinear market impact of technical traders: Empirical evidence for the US corn market”, Physica A, 349, 641–648. Westerhoff, F. and Reitz, S. (2003), “Nonlinearities and cyclical behavior: The role of chartists and fundamentalists”, Studies in Nonlinear Dynamics and Econometrics, vol. 7, no. 4.
URI:	http://wrap.warwick.ac.uk/id/eprint/1744

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