Signals of Chaotic Behavior in Middle Eastern Stock Exchanges

Abstract

This study involves analysis of financial time series using nonlinear data analysis methods involving chaos and fractal analysis methods such as R/S, DFA, attractor reconstruction using phase space representation, delay coordinates, mutual information, false nearest neighbors (henceforth referred to as FNN) and maximal Lyapunov exponents. A reparametrization of the Lyapunov exponent analysis that is addressed towards the aliasing effect frequently seen in economic time series has also been used.