Alfarano, Simone and Milakovic, Mishael and Raddant, Matthias (2011): A Note on institutional hierarchy and volatility in financial markets.

PDF
MPRA_paper_30902.pdf Download (459kB)  Preview 
Abstract
From a statistical point of view, the prevalence of nonGaussian distributions in nancial returns and their volatilities shows that the Central Limit Theorem (CLT) often does not apply in nancial markets. In this paper we take the position that the independence assumption of the CLT is violated by herding tendencies among market participants, and investigate whether a generic probabilistic herding model can reproduce nonGaussian statistics in systems with a large number of agents. It is wellknown that the presence of a herding mechanism in the model is not sucient for nonGaussian properties, which crucially depend on the details of the communication network among agents. The main contribution of this paper is to show that certain hierarchical networks, which portray the institutional structure of fund investment, warrant nonGaussian properties for any system size and even lead to an increase in systemwide volatility. Viewed from this perspective, the mere existence of nancial institutions with socially interacting managers contributes considerably to nancial volatility.
Item Type:  MPRA Paper 

Original Title:  A Note on institutional hierarchy and volatility in financial markets 
Language:  English 
Keywords:  Herding; financial volatility; networks; coreperifery 
Subjects:  C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C46  Specific Distributions ; Specific Statistics E  Macroeconomics and Monetary Economics > E1  General Aggregative Models > E19  Other D  Microeconomics > D8  Information, Knowledge, and Uncertainty > D85  Network Formation and Analysis: Theory G  Financial Economics > G1  General Financial Markets > G10  General 
Item ID:  30902 
Depositing User:  Simone Alfarano 
Date Deposited:  16. May 2011 15:02 
Last Modified:  09. Mar 2015 23:04 
References:  S. Alfarano and T. Lux. A noise trader model as a generator of apparent nancial power laws and long memory. Macroeconomic Dynamics, 11(S1): 80{101, 2007. S. Alfarano and M. Milakovic. Network structure and Ndependence in 20 agentbased herding models. Journal of Economic Dynamics and Control, 33:78{92, 2009. S. Alfarano, T. Lux, and F. Wagner. Estimation of agentbased models: The case of an asymmetric herding model. Computational Economics, 26: 19{49, 2005. S. Alfarano, T. Lux, and F. Wagner. Timevariation of higher moments in a nancial market with heterogeneous agents: An analytical approach. Journal of Economic Dynamics and Control, 32:101{136, 2008. V. Al, M. Cristelli, L. Pietronero, and A. Zaccaria. Minimal agent based model for nancial markets i. European Physical Journal B, 67:385{397, 2009. M. Aoki. Thermodynamic limits of macroeconomic or nancial models: One and twoparameter PoissonDirichlet models. Journal of Economic Dynamics and Control, 32:66{84, 2008. A. V. Banerjee. A simple model of herd behavior. Quarterly Journal of Economics, 107(3):797{817, 1992. A.L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286:509{512, 1999. S. Bikhchandani, D. Hirshleifer, and I. Welch. A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5):992{1026, 1992. S. Bornholdt. Expectation bubbles in a spin model of markets: Intermittency from frustation across scales. International Journal of Modern Physics C, 12:667{674, 2001. W. A. Brock and C. H. Hommes. A rational route to randomness. Econo metrica, 65:1059{1095, 1997. C. P. Chamley. Rational Herds. Cambridge University Press, New York, 2004. S.K. Chang. A simple asset pricing model with social interactions and heterogeneous beliefs. Journal of Economic Dynamics and Control, 31: 1300{1325, 2007. 21 R. Cont. Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2):223{236, 2001. R. Cont and J. P. Bouchaud. Herd behaviour and aggregate uctuations in nancial markets. Macroeconomic Dynamics, 4:170{196, 2000. E. Egenter, T. Lux, and D. Stauer. Finitesize eects in Monte Carlo simulations of two stock market models. Physica A, 268:250{256, 1999. X. Gabaix, P. Gopikrishnan, V. Plerou, and H. E. Stanley. A theory of powerlaw distributions in nancial market uctuations. Quarterly Jour nal of Economics, 121(2):461{504, 2006. U. Garibaldi, M. A. Penco, and P. Viarengo. An exact physical approach to market participation models. In R. Cowan and N. Jonard, editors, Heterogeneous Agents, Interactions and Economic Performances, Lecture Notes in Economics and Mathematical Systems, pages 91{103. Springer, Berlin, 2003. C. H. Hommes. Heterogeneous agent models in economics and nance. In L. Tesfatsion and K. L. Judd, editors, Handbook of Computational Eco nomics, volume 2, pages 1109{1186. NorthHolland, Amsterdam, 2006. H. Hong, J. D. Kubik, and J. C. Stein. Thy neighbor's portfolio: Wordofmouth eects in the holdings and trades of money managers. Journal of Finance, 60:2801{2824, 2005. G. Iori. A microsimulation of traders' activity in the stock market: the role of heterogeneity, agents' interactions and trade friction. Journal of Economic Behavior and Organization, 49:269{285, 2002. A. Irle, J. Kauschke, T. Lux, and M. Milakovic. Switching rates and the asymptotic behavior of herding models. Advances in Complex Systems, forthcoming, 2011. A. Kirman. Epidemics of opinion and speculative bubbles in nancial markets. In M. P. Taylor, editor, Money and Financial Markets, pages 354{ 368. Blackwell, Cambridge, 1991. A. Kirman. Ants, rationality, and recruitment. Quarterly Journal of Eco nomics, 108:137{156, 1993. 22 T. Lux and S. Schornstein. Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics, 41:169{196, 2005. M. Newman. The structure and function of complex networks. SIAM Re view, 45:167{256, 2003. A. Pagan. The econometrics of nancial markets. Journal of Empirical Finance, 3:15{102, 1996. Y. Schwarzkopf and J. D. Farmer. Time evolution of the mutual fund size distribution. arXiv/0807.3800, 2008. D. Stauer and D. Sornette. Selforganized percolation model for stock market uctuation. Physica A, 271:496{506, 1999. D. J. Watts and S. H. Strogatz. Collective dynamics of `smallworld' networks. Nature, 393:440{442, 1998. R. Wermers. Mutual fund herding and the impact on stock prices. Journal of Finance, 54(2):581{622, 1999. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/30902 