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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Hacinliyan, A.S., Aybar, O.O., Aybar, I.K., Kulali, M., Karaduman, S. (2013). Signals of Chaotic Behavior in Middle Eastern Stock Exchanges. In: Stavrinides, S., Banerjee, S., Caglar, S., Ozer, M. (eds) Chaos and Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33914-1_48
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DOI: https://doi.org/10.1007/978-3-642-33914-1_48
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