Search for content and authors
 

Hierarchical networks effects in a financial herding model

Simone Alfarano ,  Mishael Milakovic ,  Matthias Raddant 

Christian-Albrechts-Universität Kiel, Kiel 24098, Germany

Abstract

One reason for the popularity of herding models is that one can generate time-series that have statistical properties similar to those of financial market returns. This feature unfortunately vanishes once the number of agents in such a model is increased. This problem is known as N-dependence.

The hierarchical network model we propose overcomes this effect. We divide the total population of agents into two groups of which the first group forms a fully connected core which undergoes an opinion formation process, similar to the herding model of Kirman. The rest of the population, so-called followers, are each linked to only one of the core agents and have an indirect influence on the opinion formation of the core. This setup is supposed to mimic a hierarchical network in a financial market, where a relatively small number of core-agents are acting on behalf of a much greater number of followers. We assume that if a member of the core of this network changes his opinion about the state of the world, the contagious effects onto other core agents will depend on his weight, measured by the number of his followers.

In this model we analyze the effects of differently distributed followers onto the volatility of the system-wide opinion formation process.

 

Legal notice
  • Legal notice:
 

Related papers

Presentation: Oral at International Conference on Economic Science with Heterogeneous Interacting Agents 2008, by Matthias Raddant
See On-line Journal of International Conference on Economic Science with Heterogeneous Interacting Agents 2008

Submitted: 2008-03-15 15:27
Revised:   2009-06-07 00:44