Description:
1. Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System.
http://bitcoin.org/bitcoin.pdf (2009). Accessed 23 Mach 2018
2. Baumann, A., Fabian, B., Lischke, M.: Exploring the Bitcoin Network.
https://files.ifi.uzh.ch/stiller/.../WEBIST_2014_109_CR.pdf (2014). Accessed 23 Mach
2018
3. Bouri, E., Azzi, G., Dyhrberg, A.H.: On the Return-volatility Relationship in the Bitcoin
Market Around the Price Crash of 2013. Economics. 11(2017).
http://dx.doi.org/10.5018/economics-ejournal.ja.2017-2. Accessed 23 Mach 2018.
4. Hencic, A., Gourieroux, C.: Noncausal Autoregressive Model in Application to
Bitcoin/USD Exchange Rates. In: Huynh, V.-N. et al.(eds), Econometrics of Risk, Studies
in Computational Intelligence, pp.17-40. Springer, New York (2015).
5. MacDonell, A.: Popping the Bitcoin Bubble: An application of log-periodic power law
modelling to digital currency. https://economics.nd.edu/assets/134206/mac_donell_ popping_the_bitcoin_bubble_an_application_of_log_periodic_power_law_modeling_to_digit
al_currency.pdf (2014). Accessed 23 Mach 2018
6. Sornette, D.: Why Stock Markets Crash. Princeton University Press, Princeton (2017)
7. Kirichenko, L., Bulakh, V., Radivilova, T.: Fractal Time Series Analysis of Social Network Activities openarchive.nure.ua/.../4259/.../paper_110%20%281%29.pdf (2017). Accessed 23 Mach 2018
8. Bariviera, A.F.: The inefficiency of Bitcoin revisited: a dynamic approach.
arXiv:1709.08090v1 [q-fin.ST] 23 Sep 2017
9. McNally, S.: Predicting the price of Bitcoin using Machine Learning.
http://trap.ncirl.ie/2496/1/seanmcnally.pdf (2016). Accessed 20 Mach 2018
10. Tarnopolski, M.: Modeling the price of Bitcoin with geometric fractional Brownian motion: a Monte Carlo approach. arXiv:1707.03746v3 [q-fin.CP] 3 Aug 2017
11. Cocco, L., Concas, G., Marchesi, M.: Using an Artificial Financial Market for studying a
Cryptocurrency Market. arXiv:1406.6496v1 [q-fin.TR] 25 Jun 2014
12. Subelj, L., Weiss, G., Blagus, N., Bajec, M.: What coins the bitcoin? Exploratory analysis
of bitcoin market value by network group discovery. http://gw.tnode.com/networkanalysis/f/netsci2014subelj-abstract.pdf (2014). Accessed 20 Mach 2017
13. Donner, R. V., Small, M., Donges, J. F., Marwan, N., Zou, Y., Xiang,R., and Kurths, J.:
Recurrence-based time series analysis by means of complex network methods. Int. J. Bifurcat. Chaos. 21(4), 1019–1046. doi: 10.1142/S0218127411029021
14. Webber C.L., Marwan N.: Recurrence Plots and Their Quantifications: Expanding Horizons. Proceedings of the 6th International Symposium on Recurrence Plots, Grenoble,
France, 17–19 June 2015, vol. 180, pp. 1-387. Springer, Heidelberg (2016)
15. Bandt, C., Pompe, B.: Permutation entropy: A natural complexity measure for time series.
Phys. Rev. Lett. 88(17), 2-4 (2002)
16. Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Recurrence plots for the analysis of
complex systems. Phys. Rep.438(5–6), 237–329 (2007)
17. Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., Kurths, J.: Recurrence plot
based measures of complexity and its application to heart rate variability data. Phys. Rev.
E66(2), 026702 (2002)
18. Zbilut, J.P., Webber C.L.Jr.: Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A171(3–4), 199–203 (1992)
19. Webber, C.L.Jr., Zbilut, J.P.: Dynamical assessment of physiological systems and states
using recurrence plot strategies. J. Appl. Physiol.76(2), 965–973 (1994) 20. Toomey, J.P., Kane, D.M., Ackermann, T.: Complexity in pulsed nonlinear laser systems
interrogated by permutation entropy. Opt Express. 22 (2014). doi: 10.1364/OE.22.017840
21. Roberts, J.J.: 5 Big Bitcoin Crashes: What We Learned.
http://fortune.com/2017/09/18/bitcoin-crash-history/ (2017). Accessed 18 Sept 2017