Subject. This article explores the cryptocurrency market and the changes in the three most popular cryptocurrencies currently, namely Bitcoin, Ethereum and Tether, in particular. Objectives. The article aims to answer the question whether it is possible to predict the cryptocurrency rate taking into account the high market value volatility or not. Results. Testing the cryptocurrency market for information efficiency made it possible to choose the most adequate model for predicting the market prices of cryptocurrency, namely the Heterogeneous Autoregressive model of Realized Volatility – HAR-RV model. Despite the simplicity of the structure, the HAR-RV model shows good results in predicting the market prices of cryptocurrency. Taking into account that forecasting the changes in time series using regression models fails with unexpected spikes in market information, the Shannon entropy gets calculated, the values of which warn the researcher in advance about the growth or decline of the cryptocurrency rate. The article proposes to enhance the predictive properties of the HAR-RV model by calculating the Shannon information entropy for the studied time series. Conclusions and Relevance. Currently, despite the high volatility of the cryptocurrency, the changes in its market price can be predicted quite accurately. Cryptocurrency meets all the Austrian School's requirements for money, and in the future, it will be able to compete with fiat currencies significantly. The proposed method of forecasting the changes in time series can be used by analysts and traders concerning their stock, exchange, and money market activities.
Tredinnick L. Cryptocurrencies and the Blockchain. Business Information Review, 2019, vol. 36, iss. 1, pp. 39–44. URL: Link
Masciandaro D. Central Bank Digital Cash and Cryptocurrencies: Insights from a New Baumol – Friedman Demand for Money. The Australian Economic Review, 2018, vol. 51, iss. 4, pp. 540–550. URL: Link
Dyhrberg A.H. Bitcoin, Gold and the Dollar – A GARCH Volatility Analysis. Finance Research Letters, 2016, vol. 16, pp. 85–92. URL: Link
Bjerg O. How is Bitcoin Money? Theory, Culture and Society, 2016, vol. 33, iss. 1, pp. 53–72. URL: Link
Adeleke I., Zubairu U.M., Abubakar B. et al. A Systematic Review of Cryptocurrency Scholarship. International Journal of Commerce and Finance, 2019, vol. 5, iss. 2, pp. 63–75. URL: Link
Ivanchenko I.S. [Methods for testing the efficiency of the financial market]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2015, vol. 8, iss. 21, pp. 58–68. URL: Link (In Russ.)
Simanovskii A.Yu. [On the issue of crypto-currency economic nature]. Voprosy Ekonomiki, 2018, no. 9, pp. 132–142. (In Russ.) URL: Link
Stolbov M.I. [On some implications of blockchain for financial sector]. Voprosy Ekonomiki, 2018, no. 6, pp. 133–145. (In Russ.) URL: Link
Stolbov M.I. [The 10th anniversary of the cryptocurrency market: Its current state and prospects]. Voprosy Ekonomiki, 2019, no. 5, pp. 136–148. (In Russ.) URL: Link
Shaidullina V.K. [Cryptocurrency: Development forecast in the conditions of the modern financial market]. Ekonomicheskie nauki = Economic Sciences, 2018, no. 12, pp. 106–111. URL: Link (In Russ.)
Erdas M.L., Caglar A.E. Analysis of the Relationships Between Bitcoin and Exchange Rate, Commodities and Global Indexes by Asymmetric Causality Test. Eastern Journal of European Studies, 2018, vol. 9, iss. 2, pp. 27–45. URL: Link
Williamson S. Is Bitcoin a Waste of Resources? Federal Reserve Bank of St. Louis Review, 2018, vol. 100, no. 2, pp. 107–115. URL: Link
Phillip A., Chan J., Peiris S. On Long Memory Effects in the Volatility Measure of Cryptocurrencies. Finance Research Letters, 2019, vol. 28, pp. 95–100. URL: Link
Zhilkin A.N. [Are cryptocurrencies able to displace dollars in international payments?]. Vestnik Evraziiskoi nauki, 2018, vol. 10, no. 5. (In Russ.) URL: Link
Kjærland F., Khazal A., Krogstad E.A. et al. An Analysis of Bitcoin's Price Dynamics. Journal of Risk and Financial Management, 2018, vol. 11, iss. 4. URL: Link
Mangla N., Bhat A., Avabratha G., Bhat N. Bitcoin Price Prediction Using Machine Learning. International Journal of Information and Computing Science, 2019, vol. 6, iss. 5, pp. 318–320. URL: Link
Yecheng Yao, Jungho Yi, Shengjun Zhai et al. Predictive Analysis of Cryptocurrency Price Using Deep Learning. International Journal of Engineering and Technology, 2018, vol. 7, no. 3.27, pp. 258–264. URL: Link
Valencia F., Gómez-Espinosa A., Valdés-Aguirre B. Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning. Entropy, 2019, vol. 21, iss. 6. URL: Link
Kjærland F., Meland M., Oust A., Oyen V. How can Bitcoin Price Fluctuations be Explained? International Journal of Economics and Financial Issues, 2018, vol. 8, iss. 3, pp. 323–332. URL: Link
Corbet S., McHugh G., Meegan A. The Influence of Central Bank Monetary Policy Announcements on Cryptocurrency Return Volatility. Investment Management and Financial Innovations, 2017, vol. 14, iss. 4, pp. 60–72. URL: Link.2017.07
Kristjanpoller W., Minutolo M.C. A Hybrid Volatility Forecasting Framework Integrating GARCH, Artificial Neural Network, Technical Analysis and Principal Components Analysis. Expert Systems with Applications, 2018, vol. 109, pp. 1–11. URL: Link
Pele D.T., Mazurencu-Marinescu-Pele M. Using High-Frequency Entropy to Forecast Bitcoin's Daily Value at Risk. Entropy, 2019, vol. 21, iss. 2. URL: Link
Andersen T.G., Bollerslev T., Diebold F.X. Roughing it Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility. The Review of Economics and Statistics, 2007, vol. 89, iss. 4, pp. 701–720. URL: Link
Ivanchenko I.S. [On the monetary functions of cryptocurrency]. Finansy i kredit = Finance and Credit, 2019, vol. 25, iss. 10, pp. 2369–2384. (In Russ.) URL: Link
Corsi F. A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 2009, vol. 7, iss. 2, pp. 174–196. URL: Link
Andersen T.G., Bollerslev T., Diebold F.X., Labys P. The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 2001, vol. 96, iss. 453, pp. 42–55. URL: Link
Hayek F.A. Chastnye den'gi [The Denationalisation of Money]. Moscow, Institut natsional'noi modeli ekonomiki Publ., 1996, 118 p.
Daw C.S., Finney C.E.A., Tracy E.R. A Review of Symbolic Analysis of Experimental Data. Review of Scientific Instruments, 2003, vol. 74, iss. 2, pp. 915–930. URL: Link