Dynamics of quasi-stationary systems: Finance as an example
1 Institute of Physics and ForWind, Carl-von-Ossietzky University Oldenburg - Oldenburg, Germany
2 Faculty of Physics, University of Duisburg-Essen - Duisburg, Germany
Received: 9 March 2015
Accepted: 12 June 2015
We propose a combination of cluster analysis and stochastic process analysis to characterize high-dimensional complex dynamical systems by few dominating variables. As an example, stock market data are analyzed for which the dynamical stability as well as transitions between different stable states are found. This combined method allows especially to set up new criteria for merging clusters to uncover dynamically distinct states. The low-dimensional approach allows to recover the high-dimensional fixed points of the system by means of an optimization procedure.
PACS: 89.75.-k – Complex systems / 05.45.-a – Nonlinear dynamics and chaos / 02.50.Fz – Stochastic analysis
© EPLA, 2015