Dynamic characteristic of Bitcoin cryptocurrency in the reconstruction scheme
暂无分享,去创建一个
[1] L. G. S. Duarte,et al. Alternative predictors in chaotic time series , 2017, Comput. Phys. Commun..
[2] A. H. Dyhrberg. Bitcoin, gold and the dollar – A GARCH volatility analysis , 2016 .
[3] Salim Lahmiri,et al. Chaos, randomness and multi-fractality in Bitcoin market , 2018 .
[4] María José Basgall,et al. Some Stylized Facts of the Bitcoin Market , 2017, 1708.04532.
[5] Kenneth F. Wallis,et al. Density Forecasting: A Survey , 2000 .
[6] D. Ruelle. Chaotic evolution and strange attractors : the statistical analysis of time series for deterministic nonlinear systems , 1989 .
[7] S. Lahmiri,et al. The high frequency multifractal properties of Bitcoin , 2019, Physica A: Statistical Mechanics and its Applications.
[8] J. Yorke,et al. Coping with chaos. Analysis of chaotic data and the exploitation of chaotic systems , 1994 .
[9] Eng-Tuck Cheah,et al. Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin , 2015 .
[10] Salim Lahmiri,et al. Cryptocurrency forecasting with deep learning chaotic neural networks , 2019, Chaos, Solitons & Fractals.
[11] P. Royston. Approximating the Shapiro-Wilk W-test for non-normality , 1992 .
[12] P.R.L. Alves,et al. Detecting chaos and predicting in Dow Jones Index , 2018 .
[13] H. Kantz,et al. Nonlinear time series analysis , 1997 .
[14] L. G. S. Duarte,et al. A new method for improved global mapping forecast , 2016, Comput. Phys. Commun..
[15] F. Pukelsheim. The Three Sigma Rule , 1994 .
[16] P.R.L. Alves,et al. A new characterization of chaos from a time series , 2017 .
[17] S. Nadarajah,et al. On the inefficiency of Bitcoin , 2017 .
[18] L. G. S. Duarte,et al. Improvement in global forecast for chaotic time series , 2016, Comput. Phys. Commun..
[19] P.R.L. Alves,et al. The goodness of global fitting in the reconstruction scheme , 2020, Comput. Phys. Commun..
[20] Farmer,et al. Predicting chaotic time series. , 1987, Physical review letters.
[21] Salim Lahmiri,et al. Long-range memory, distributional variation and randomness of bitcoin volatility , 2018 .
[22] Holger Kantz,et al. Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.
[23] L. G. S. Duarte,et al. A Maple package for improved global mapping forecast , 2014, Comput. Phys. Commun..
[24] Mantegna,et al. Variety and volatility in financial markets , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[25] P. Alves. Chaos in historical prices and volatilities with five-dimensional euclidean spaces , 2019, Chaos, Solitons & Fractals: X.
[26] S. Lahmiri,et al. Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering , 2019, Chaos, Solitons & Fractals.
[27] Robert Hudson,et al. Calculating and Comparing Security Returns is Harder than you Think: A Comparison between Logarithmic and Simple Returns , 2010 .
[28] Kevin James Daly,et al. Financial volatility : issues and measuring techniques , 2008 .
[29] Igor Klioutchnikov,et al. Chaos Theory in Finance , 2017 .
[30] Andrew Urquhart. The Inefficiency of Bitcoin , 2016 .
[31] F. Takens. Detecting strange attractors in turbulence , 1981 .