Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал:
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/2851| Назва: | Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors |
| Автори: | Соловйов, Володимир Миколайович Бєлінський, Андрій Олександрович |
| Ключові слова: | cryptocurrency bitcoin complex system measures of complexity nonlinear dynamics recurrence plot recurrence quantification analysis entropy permutation entropy crisis indicator-precursor |
| Дата публікації: | 2018 |
| Бібліографічний опис: | Soloviev V. N. Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors [Electronic resource] / Vladimir Soloviev, Andrey Belinskij // ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer 2018 : Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops (ICTERI, 2018). Kyiv, Ukraine, May 14-17, 2018 / Edited by : Vadim Ermolayev, Mari Carmen Suárez-Figueroa, Vitaliy Yakovyna, Vyacheslav Kharchenko, Vitaliy Kobets, Hennadiy Kravtsov, Vladimir Peschanenko, Yaroslav Prytula, Mykola Nikitchenko, Aleksander Spivakovsky. – P. 116-127. – (CEUR Workshop Proceedings (CEUR-WS.org), Vol. 2104). – Access mode : http://ceur-ws.org/Vol-2104/paper_175.pdf |
| Короткий огляд (реферат): | This article demonstrates the possibility of constructing indicators of critical and crisis phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex systems such as recurrent analysis of dynamic systems and the calculation of permutation entropy are used. It is shown that it is possible to construct dynamic measures of complexity, both recurrent and entropy, which behave in a proper way during actual pre-crisis periods. This fact is used to build predictors of crisis phenomena on the example of the main five crises recorded in the time series of the key cryptocurrency bitcoin, the effectiveness of the proposed indicators-precursors of crises has been identified. |
| Опис: | 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 |
| URI (Уніфікований ідентифікатор ресурсу): | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/2851 https://doi.org/10.31812/123456789/2851 |
| ISSN: | 1613-0073 |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| paper_175.pdf | 610 kB | Adobe PDF | Переглянути/Відкрити |
Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.