Finance and Credit
 

Developing a stochastic model for medium-term forecasting of cryptocurrency exchange rate: The bitcoin case

Vol. 24, Iss. 5, MAY 2018

Received: 20 April 2018

Received in revised form: 4 May 2018

Accepted: 18 May 2018

Available online: 29 May 2018

Subject Heading: INTERNATIONAL PAYMENT MECHANISMS

JEL Classification: F47, F63, G17

Pages: 1046-1060

https://doi.org/10.24891/fc.24.5.1046

Safiullin M.R. Kazan (Volga Region) Federal University, Kazan, Republic of Tatarstan, Russian Federation
Marat.Safiullin@tatar.ru

https://orcid.org/0000-0003-3708-8184

El'shin L.A. University of Management TISBI, Kazan, Zelenodolsk, Republic of Tatarstan, Russian Federation
Leonid.Elshin@tatar.ru

https://orcid.org/0000-0002-0763-6453

Abdukaeva A.A. Center of Advanced Economic Research in the Academy of Sciences of the Republic of Tatarstan, Kazan, Republic of Tatarstan, Russian Federation
Aliya.Abdukaeva@tatar.ru

https://orcid.org/0000-0003-1262-5588

Importance The article considers the process of economic and mathematical modeling of time series characterizing the volatility of bitcoin exchange rate on the basis of autoregressive moving average models.
Objectives The purpose of the work is to provide a scientific rationale for tools and mechanisms to forecast the cryptocurrency market development.
Methods The study rests on tools of stochastic analysis of stationary and non-stationary time series characterizing the volatility in the global cryptocurrency market.
Results We prove that ARIMA models enable to predict current and future adjustments of cryptocurrency exchange rates with a high level of accuracy.
Conclusions and Relevance We found that by the end of the third quarter of 2018, the bitcoin market value will be about 11,000 USD. The methodological approaches of modeling help define future trends and fluctuations of exchange rates over the entire forecast period. The findings are of practical interest for both the public authorities and representatives of business community.

Keywords: cryptocurrency market, forecasting, time series modeling, stochastic analysis, bitcoin

References:

  1. Lo S., Wang C.J. Bitcoin as Money? Federal Reserve Bank of Boston: Current Policy Perspectives, 2014, no. 2014-4.
  2. 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: https://doi.org/10.1016/j.dss.2016.12.001
  3. Nakamoto S. Bitcoin: A Peer- to-Peer Electronic Cash System. URL: https://bitcoin.org/bitcoin.pdf
  4. Bouoiyour J., Selmi R. Bitcoin Price: Is it Really that New Round of Volatility Can Be on Way? MPRA Paper, 2015. URL: https://mpra.ub.uni-muenchen.de/id/eprint/65580
  5. 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: https://doi.org/10.1016/j.tele.2016.05.005
  6. 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: https://doi.org/10.14257/ijsia.2016.10.11.18
  7. Luther W. Cryptocurrencies, Network Effects, and Switching Costs. Contemporary Economic Policy, 2016, no. 34(3), 553–571. URL: https://doi.org/10.1111/coep.12151
  8. Vranken H. Sustainability of Bitcoin and Blockchains. Current Opinion in Environmental Sustainability, 2017, no. 28, pp. 1–9. URL: https://doi.org/10.1016/j.cosust.2017.04.011
  9. Wang H., He D., Ji Y. Designated-Verifier Proof of Assets for Bitcoin Exchange Using Elliptic Curve Cryptography. Future Generation Computer Systems, 2017. URL: https://doi.org/10.1016/j.future.2017.06.028
  10. Wilson M., Yelowitz A. Characteristics of Bitcoin Users: An Analysis of Google Search Data. URL: http://dx.doi.org/10.2139/ssrn.2518603
  11. 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: https://doi.org/10.1016/j.physa.2017.04.159
  12. White L.H. The Market for Cryptocurrencies. GMU Working Paper in Economics, 2014, no. 14-45, 27 p. URL: https://doi.org/10.2139/ssrn.2538290
  13. 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: https://doi.org/10.1016/j.econlet.2015.02.029
  14. Perron P. Further Evidence on Breaking Trend Functions in Macroeconomic Variables. Journal of Econometrics, 1997, vol. 80, iss. 2, pp. 355–385. URL: https://doi.org/10.1016/S0304-4076(97)00049-3
  15. Box G., Jenkins G., Reinsel G. Analiz vremennykh ryadov. Prognoz i upravlenie. [Time Series Analysis: Forecasting and Control]. Moscow, Mir Publ., 1974.
  16. 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: https://doi.org/10.1016/0304-4076(94)01632-A

View all articles of issue

 

ISSN 2311-8709 (Online)
ISSN 2071-4688 (Print)

Journal current issue

Vol. 24, Iss. 7
July 2018

Archive