Authors
- Temirova Aza Barnovna Baronovna
- Israilov Albert Ruslanovich
Annotation
The article examines the concept of «cryptocurrency», substantiates the popularity of this new type of financial instrument in the economic space, highlights the main features of cryptocurrency as one of the types of virtual money and its impact on the economic system. The following key features of cryptocurrencies are highlighted: exchange for goods or services; exchange for classic currency; payment for goods and services; payments, fast and direct transactions; investment asset. The article examines the main directions of the formation of the cryptocurrency market as a component of the financial
market. The essence and significance of cryptocurrencies are defined, the prerequisites for the emergence of cryptocurrencies are characterized, and the characteristics of the cryptocurrency market as a component of the financial market are given. The stages of formation and the theoretical prerequisites for the emergence of cryptocurrencies are investigated.
It has been established that the main direction of the functioning and use of such a specific financial asset is the protection of funds from depreciation, which occurs during political and economic fluctuations. The purpose of the study is a comparative analysis of approaches to analyzing trends in the dynamics of cryptocurrencies. The paper analyzes the trends in the development of cryptocurrencies, which have shown an increase in the influence of cryptocurrencies on the structure of the financial market. The works of researchers on the analysis of trends in cryptocurrency exchange rates have been studied and three main approaches to analysis have been formed.
How to link insert
Temirova, A. B. & Israilov, A. R. (2025). ANALYSIS OF APPROACHES AND THEIR FEATURES FOR STUDYING THE DYNAMICS OF CRYPTOCURRENCIES Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", № 2 (44), 10. https://doi.org/10.24412/2312-6647-2025-244-10-23
References
1.
1. Bitcoin pricing, adoption, and usage: Theory and evidence / S. Athey [et al.] // Stanford University, Graduate School of Business. Research Paper. 2016. № 16-42. URL: http://ssrn.com/abstract=2826674
2.
2. The blockchain folk theorem / B. Biais [et al.] // The Review of Fi-nancial Studies. 2019. № 32 (5). P. 1662–1715. DOI: 10.1093/rfs/hhy095
3.
3. Equilibrium bitcoin pricing / B. Biais [et al.] // Journal of Finance. 2023. № 78 (2). P. 967–1014. DOI: 10.1111/JOFI.13206
4.
4. Byström H., Krygier D. What drives bitcoin volatility? // Working Papers. 2018. № 24.
5.
5. Charles A., Darné O. Volatility estimation for Bitcoin: Replication and robustness // International Economics. 2019. № 157. P. 23–32.
6.
6. Token-based platform finance / L. W. Cong [et al.] // Journal of Fi-nancial Economics. 2022. № 144. P. 972–991. DOI: 10.1016/j.jfineco.2021.10.002
7.
7. Cong L.W., He Z., Li J. Decentralized mining in centralized pools // The Review of Financial Studies. 2021. № 34 (3). P. 1191–1235. DOI: 10.1093/rfs/hhaa040
8.
8. Easley D., O’Hara M., Basu S. From mining to markets: The evolution of bitcoin transaction fees // Journal of Financial Economics. 2019. № 134 (1). P. 91–109. DOI: 10.1016/j.jfineco.2019.03.004
9.
9. How many cryptocurrencies are there in 2024? // Exploding Topics. URL: https://
explodingtopics.com/blog/number-of-cryptocurrencies (accessed: 12.06.2025).
10.
10. Predicting the price of Bitcoin using sentiment-enriched time series forecasting / M. Frohmann [et al.] // Big Data and Cognitive Computing. 2023. № 7 (3). P. 137–156.
11.
11. Gers F. A., Eck D., Schmidhuber J. Applying LSTM to Time Series Predictable through Time-Window Approaches // Artificial Neural Networks — ICANN 2001. Lecture Notes in Computer Science. Vol. 2130. Springer, Berlin, Heidelberg. DOI: 10.1007/3-540-44668-0_93
12.
12. Hamayel M. J., Owda A. Y. A novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms // Artificial Intelligence. 2021. № 2 (4). P. 477–496.
13.
13. Jaquart P., Köpke S., Weinhardt C. Machine learning for crypto-currency market prediction and trading // The Journal of Finance and Data Science. 2022. № 8. P. 331–352.
14.
14. Kelly B. The bitcoin big bang: how alternative currencies are about to change the world. John Wiley & Sons, 2014. 243 p.
15.
15. Priorities for the development of regional information management based on system digitalisation / Yu. Kovalenko et al. // SHS Web of Conferences. 2021. Vol. 126. Article № 04002. DOI: 10.1051/shsconf/202112604002
16.
16. Liu Y., Tsyvinski A. Risks and returns of cryptocurrency // The Review of Financial Studies. June 2021. Vol. 34. Issue 6. P. 2689–2727. DOI: 10.1093/rfs/ hhaa113
17.
17. Liu Y., Tsyvinski A., Wu X. Common risk factors in cryptocurrency // Journal of Finance. 2022. № 77 (2). Р. 1133–1177. DOI: 10.1111/JOFI.13119
18.
18. Forecast methods for time series data: A survey / Z. Liu [et al.] // IEEE Access. 2021. № 9. Р. 91896–91912.
19.
19. Time-series forecasting of Bitcoin prices using high-dimensional features: A machine learning approach / M. Mudassir [et al.] // Neural Com-puting & Applications. 2020. Р. 1–15.
20.
20. Pagnotta E. S. Decentralizing money: Bitcoin prices and block-chain security // The Review of Financial Studies. 2022. № 35 (2). Р. 866–907. DOI: 10.1093/rfs/hhaa149
21.
21. Schilling L., Uhlig H. Some simple bitcoin economics // The Journal of Monetary Economics. 2019. № 106. Р. 16–26. DOI: 10.1016/j.jmoneco.2019.07.002
22.
22. Vneshnetorgovaya politika i ee vliyanie na e`konomicheskij rost / X. Tel`nova [i dr.] // Problemy` teorii i praktiki finansovo-kreditnoj deyatel`nosti. 2023. № 4 (51). Р. 345–357. DOI: 10.55643/fcaptp.4.51.2023.4097
23.
23. Forecasting Bitcoin closing price series using linear regression and neural networks models / N. Uras [et al.] // PeerJ Computer Science. 2020. № 6. 6:e279. DOI: 10.7717/peerj-cs.279
24.
24. Regional`naya politika peredovy`x IKT v soznanii formiruyushhejsya e`konomiki znanij / O. Zaxarova [i dr.] // Integraciya, garmonizaciya i peredacha znanij: materialy` 16-j Mezhdunar. konf. po IKT v obrazovanii, nauchny`x issledovaniyax i promy`shlennom primenenii (Xar`kov, Ukraina). T. I: Osnovnaya konferenciya. ICTERI. Xar`kov, 2020. T. 2740. S. 386–391.