Importance The article discusses the process of economic and mathematical modeling of time series describing the volatility of the bitcoin exchange rate through the Autoregressive Moving Average (ARMA) models. Objectives We search for, and substantiate tools and mechanisms used to predict the cryptocurrency market developments. Methods The research applies tools of stochastic analysis of stationary and non-stationary time series. Results The ARIMA models provide for rather precise estimates of current and future changes in the digital money rates for a three to four month’s time. Conclusions and Relevance The bitcoin price will have approximated USD 11,000 by the end of Q3 2018. The methodological approaches to modeling help determine not only future trends, but also changes in exchange rates throughout the entire analyzable period. The findings provide empirical information for cryptocurrency market regulators and business community.
Lo S., Wang C.J. Bitcoin as Money? Federal Reserve Bank of Boston: Current Policy Perspectives, 2014, no. 2014-4.
Li X., Wang Ch.A. The Technology and Economic Determinants of Cryptocurrency Exchange Rates: The Case of Bitcoin. Decision Support Systems, 2017, vol. 95, pp. 49–60. URL: Link
Nakamoto S. Bitcoin: A Peer-to-Peer Electronic Cash System. URL: Link
Bouoiyour J., Selmi R. Bitcoin Price: Is It Really that New Round of Volatility Can Be on Way? MPRA Paper, 2015. URL: Link
Hayes A.S. Cryptocurrency Value Formation: An Empirical Study Leading to a Cost of Production Model for Valuing Bitcoin. Telematics and Informatics, 2017, vol. 34, iss. 7, pp. 1308–1321. URL: Link
Kim K.J., Hong S.P. Study on Rule-based Data Protection System Using Blockchain in P2P Distributed Networks. International Journal of Security and its Application, 2016, vol. 10, iss. 11, pp. 201–210. URL: Link
Luther W. Cryptocurrencies, Network Effects, and Switching Costs. Contemporary Economic Policy, 2016, no. 34(3), pp. 553–571. URL: Link
Vranken H. Sustainability of Bitcoin and Blockchains. Current Opinion in Environmental Sustainability, 2017, no. 28, pp. 1–9. URL: Link
Wang H., He D., Ji Y. Designated-Verifier Proof of Assets for Bitcoin Exchange Using Elliptic Curve Cryptography. Future Generation Computer Systems, 2017. URL: Link
Wilson M., Yelowitz A. Characteristics of Bitcoin Users: An Analysis of Google Search Data. URL: Link
Bariviera A.F., Basgall M.J., Hasperué W. et al. Some Stylized Facts of the Bitcoin Market. Physica A: Statistical Mechanics and its Application, 2017, vol. 484, pp. 82–90. URL: Link
White L.H. The Market for Cryptocurrencies. GMU Working Paper in Economics, 2014, no. 14-45, 27 p. URL: Link
Cheah E.T., Fry J. Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into the Fundamental Value of Bitcoin. Economics Letters, 2015, no. 130, pp. 32–36. URL: Link
Perron P. Further Evidence on Breaking Trend Functions in Macroeconomic Variables. Journal of Econometrics, 1997, vol. 80, iss. 2, pp. 355–385. URL: Link00049-3
Box G., Jenkins G., Reinsel G. Analiz vremennykh ryadov. Prognoz i upravlenie [Time Series Analysis: Forecasting and Control]. Moscow, Mir Publ., 1974.
Granger C.W.J., King M.L., White H. Comments on Testing Economic Theories and the Use of Model Selection Criteria. Journal of Econometrics, 1995, vol. 67, iss. 1, pp. 173–187. URL: Link01632-A
