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.
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