This work is licensed under a
Creative Commons Attribution 4.0 International License
[1] W.D. Dechert, R. Gençay, Is the largest Lyapunov exponent preserved in embedded dynamics? Physics Letters. A 276 (2000) 59-64.
[2] A. Das, P. Das, Does composite index of NYSE represents chaos in the long time scale? Applied Mathematics and Computation. 174 (2006), 483-489.
[3] A. Das, P. Das, Chaotic analysis of the foreign exchange rates, Applied Mathematics and Computation. 185 (2007), 388-396.
[4] A. Moeini, M. Ahrari, S.S. Madarshahi, Investigating Chaos in Tehran Stock Exchange Index, Iranian economic review. 18 (2007), 103-120.
[5] S. Gunay, Chaotic Structure of the BRIC Countries and Turkey's Stock Market. International Journal of Economics and Financial Issues, 5(2) (2015), 515-522.
[6] H.D. Abarbanel, Analysis of Observed Chaotic data, Springer, New York, (1996).
[7] C. Francq and J.M. Zakoian. GARCH Models Structure, Statistical Inference and Financial Applications, Wiley, (2010).
[8] R. T. Baillie, T. Bollerslev, H. O. Mikkelsen, Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, (1996), 74.
[9] R. T. Baillie, Y. W. Han, R. J. Myers Long Memory and FIGARCH Models for Daily and High Frequency Commodity Prices. Working paper (2007) No. 594.
[10] D. O. Cujaeiro, B. M. Tabak, Testing for long-range dependence in world stock markets. Chaos, Solitons and Fractals, 37 (2008), 918-927.
[11] N. Sviridova, K. Sakai, Human photoplethysmogram: new insight into chaotic characteristics. Chaos, Solitons and Fractals 77 (2015), 53-63.
[12] C. Brooks, Chaos in foreign exchange markets: a sceptical view. Computational Economics, 11 (3) (1998), 265-281.
[13] M. Frezza, Goodness of fit assessment for a fractal model of stock markets. Chaos, Solitons and Fractals. 66(2014), 41-50.
[14] E. J. Kostelich, H. L. Swinney Practical Considerations in Estimating Dimension from Time Series Data. Physica Scripta, Vol. 40 (1989), 436-441.
[15] H.F. Liu, Z.H. Dai, W.F. Li, X. Gong, Z.H. Yu. Noise robust estimates of the largest Lyapunov exponent. Physics Letters A 341 (2005), 119-127.
[16] A Wolf, J.B. Swift, H. Swinney, J. Vastano, Determining Lyapunov exponents from a time series. Physica D 16 (1985), 285-317.
[17] H. Kantz, A robust method to estimate the maximal Lyapunov exponent of a time series. Phys. Lett. A 185 (1994) 77-87.
[18] M. T. Rosenstein, J.J. Collins and J.D.L. Carlo . A Practical Method for Calculating Largest Lyapunov Exponents from small data sets. Physica D 65 (1993).
[19] T. Schreiber, Interdisciplinary application of nonlinear time series methods. Physics Reports 308 (1999), 1-64.
[20] A. M. Fraser and H. L. Swinney Independent coordinates for strange attractors from mutual information Phys. Rev. A 33 (1986) 1134-40.
[21] F. Takens, Detecting strange attractors in turbulence. In Dynamical Systems and Turbulence, Rand D, Young L (eds), Springer-Verlag, Berlin (1981).
[22] S. Kodba, M. Perc, M. Marhl, Detecting Chaos from a Time Series. European Journal of Physics 26 (2005) 205-215.
[23] M.B. Kennel, R. Brown, H.D.I. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical reconstruction. Physical Review A, V. 45 (1992).
[24] Software package on https: /www. kevinsheppard. com/MFE_Toolbox.
[25] Software package on http: /www. oxmetrics. net/index. html.
[26] Software package on http: /www. matjaperc. com/ejp/time. html.
[27] Software package on https: /www. r-project. org.
[1] A. Yilmaz, G. Unal, "Multiscale Correlation Dimension Method", International Journal of Modern Physics C, 2019
DOI: https://doi.org/10.1142/S012918312050045X