On the 25th of October, 2011 conference dedicated to the project Science for business and society will be held at hall in the Vilnius University, Institute of Theoretical Physics and Astronomy (A. Goštauto g. 12 - 432, Vilnius). This conference will mark ending of the currently ongoing project behind the Physics of Risk website. Main focus of this conference will be presentation of achieved results and discussion between business and scientists.

We have contributed two presentations towards the recent 39th Lithuanian National Physics Conference, which was organized by Vilnius University and Lithuanian Physicist Society. Oral presentation by A. Kononovicius was based on some of the models presented on Physics of Risk website, while poster presentation by R. Kazakevičius tackles very general problem related to the Physics of Risk.

As we have seen previously application of the original Kirman's model enables reproduction of single power law spectral density [1]. While actual financial markets and sophisticated stochastic models [2] have double power law spectral density - i.e., fractured spectral density. Thus it would be nice to obtain fracture of spectral density by improving application of Kirman's agent based model towards financial markets.

Time series obtained by solving non-linear stochastic models exhibit rather interesting statistical properties. On Physics of Risk we have already discussed some of these models [1, 2] (ex. stochastic model of return, herding model of financial markets), which are able to reproduce statistical properties of high frequency return (namely spectral density and probability distribution).

In statistical sense model and financial market behavior might be studied in many different manners. One may study probability distributions, moments, spectral densities, autocorrelations and etc., using each of them to obtain vital information on the statistical and dynamical properties of the studied system. It is important to note that new useful information might be provided by the statistical indicators, which are related to the previously used indicators in unambiguous manner. One may also introduce new variables describing system itself or its time series.

There is a group of such variables, which is closely related to the estimation of risk, known as burst statistics [3, 4]. In this text we will discuss these variables and their statistical properties. At the end of the text we also present an interactive HTML5 applet, using which one can reproduce burst statistics of certain stochastic model.

Kirman's ant colony model, previously presented on our website as agent based (based on [1]) and stochastic (based on [2, 3]) model, has become classical example of herding modeling. Application of this model towards economic, financial or other social scenarios might seem doubtful as human society is far more complex than ant colony, but methodologically it is more useful to start from very simple and stylized model and later add complexity on top of it. Furthermore we have already shown that Kirman's herding dynamics could be applicable in agent-based marketing (see comparison of Kirman's and Bass diffusion model). In this text we will consider financial market scenario and obtain stochastic differential equations similar to the existing stochastic models considered in [4, 5].