1. Заров И.К. Основные направления оценки рисков кредитных организаций при осуществлении вложений в цифровые финансовые активы // Экономика и управление: проблемы, решения. 2025. Т. 3. № 7 (160). С. 197-204. DOI:https://doi.org/10.36871/ek.up.p.r.2025.07.03.020. EDN: https://elibrary.ru/FEJNOT

2. Сидоров А.А., Бурцева Т.А., Дарда Е.С., Параскевопуло О.Р. Математическое моделирование аномальных доходностей криптовалют под воздействием инфоповодов // Московский экономический журнал. 2026. № 2. С. 204-241. DOI:https://doi.org/10.55186/2413046X_2026_11_2_27. EDN: https://elibrary.ru/ZESDHX

3. Шувалова Е.Б., Заров И.К. Цифровые финансовые активы на денежные требования: управление рисками и использование для секьюритизации кредитных требований банков // Экономика и управление: проблемы, решения. 2023. Т. 1. № 9 (139). С. 184-194. DOI:https://doi.org/10.36871/ek.up.p.r.2023.09.01.021. EDN: https://elibrary.ru/HVIPTK

4. Bergsli L.O., Lind A.F., Molnar P., Polasik M. Forecasting volatility of Bitcoin // Research in International Business and Finance. 2022. Vol. 59. Art. 101540. DOI:https://doi.org/10.1016/j.ribaf.2021.101540. EDN: https://elibrary.ru/BPDNCA

5. Biais B., Capponi A., Cong L.W., Gaur V., Giesecke K. Advances in blockchain and crypto economics // Management Science. 2023. Vol. 69. No. 11. P. 6417-6426. DOI:https://doi.org/10.1287/mnsc.2023.intro.v69.n11. EDN: https://elibrary.ru/OYAOQU

6. Bouteska A., Abedin M.Z., Hajek P., Yuan K. Cryptocurrency price forecasting - a comparative analysis of ensemble learning and deep learning methods // International Review of Financial Analysis. 2024. Vol. 92. Art. 103055. DOI:https://doi.org/10.1016/j.irfa.2023.103055. EDN: https://elibrary.ru/BZUJKN

7. Brauneis A., Sahiner M. Crypto volatility forecasting: mounting a HAR, sentiment, and machine learning horserace // Asia-Pacific Financial Markets. 2026. Vol. 33. No. 1. P. 379-411. DOI:https://doi.org/10.1007/s10690-024-09510-6. EDN: https://elibrary.ru/PALKNJ

8. Budish E. Trust at scale: the economic limits of cryptocurrencies and blockchains // The Quarterly Journal of Economics. 2025. Vol. 140. No. 1. P. 1-62. DOI:https://doi.org/10.1093/qje/qjae033. EDN: https://elibrary.ru/QLQBMR

9. Chen Z. From disruption to integration: cryptocurrency prices, financial fluctuations, and macroeconomy // Journal of Risk and Financial Management. 2025. Vol. 18. No. 7. Art. 360. DOI:https://doi.org/10.3390/jrfm18070360. EDN: https://elibrary.ru/KCSRVX

10. Coulter K.A. The impact of news media on Bitcoin prices: modelling data driven discourses in the crypto-economy with natural language processing // Royal Society Open Science. 2022. Vol. 9. No. 4. Art. 220276. DOI:https://doi.org/10.1098/rsos.220276. EDN: https://elibrary.ru/BOFPNJ

11. Gneiting T., Raftery A.E. Strictly proper scoring rules, prediction, and estimation // Journal of the American Statistical Association. 2007. Vol. 102. No. 477. P. 359-378. DOI:https://doi.org/10.1198/016214506000001437. EDN: https://elibrary.ru/XTNLOB

12. Golnari A., Komeili M.H., Azizi Z. Probabilistic deep learning and transfer learning for robust cryptocurrency price prediction // Expert Systems with Applications. 2024. Vol. 255. Art. 124404. DOI:https://doi.org/10.1016/j.eswa.2024.124404. EDN: https://elibrary.ru/WZSCZZ

13. Hassani H., Huang X., Silva E. Big-Crypto: big data, blockchain and cryptocurrency // Big Data and Cognitive Computing. 2018. Vol. 2. No. 4. Art. 34. DOI:https://doi.org/10.3390/bdcc2040034. EDN: https://elibrary.ru/RTUZXJ

14. Hong S., An S., Jeon J.J. Cryptocurrency price forecasting using variational autoencoder with versatile quantile modeling // Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024. P. 4530-4537. DOI:https://doi.org/10.1145/3627673.3680027.

15. Huang Z.-C., Sangiorgi I., Urquhart A. Forecasting Bitcoin volatility using machine learning techniques // Journal of International Financial Markets, Institutions and Money. 2024. Vol. 97. Art. 102064. DOI:https://doi.org/10.1016/j.intfin.2024.102064. EDN: https://elibrary.ru/RBTQYP

16. Huynh T.L.D., Shahbaz M., Nasir M.A., Ullah S. Financial modelling, risk management of energy instruments and the role of cryptocurrencies // Annals of Operations Research. 2022. Vol. 313. No. 1. P. 47-75. DOI:https://doi.org/10.1007/s10479-020-03680-y. EDN: https://elibrary.ru/ZPYDTO

17. Janjua L.R., Gigauri I., Wojcik-Czerniawska A., Pohulak-Zoledowska E. Risk management in the area of Bitcoin market development: example from the USA // Risks. 2024. Vol. 12. No. 4. Art. 67. DOI:https://doi.org/10.3390/risks12040067. EDN: https://elibrary.ru/WMFTMK

18. Kehinde T.O., Adedokun O.J., Joseph A., Kabirat K.M., Akano H.A., Olanrewaju O.A. Helformer: an attention-based deep learning model for cryptocurrency price forecasting // Journal of Big Data. 2025. Vol. 12. Art. 81. DOI:https://doi.org/10.1186/s40537-025-01135-4. EDN: https://elibrary.ru/AOYTMX

19. Khan F.U., Khan F., Shaikh P.A. Forecasting returns volatility of cryptocurrency by applying machine learning technique // Future Business Journal. 2023. Vol. 9. No. 1. Art. 40. DOI:https://doi.org/10.1186/s43093-023-00200-9. DOI: https://doi.org/10.1186/s43093-023-00221-4; EDN: https://elibrary.ru/KOKYLY

20. Koenker R., Hallock K.F. Quantile regression // Journal of Economic Perspectives. 2001. Vol. 15. No. 4. P. 143-156. DOI:https://doi.org/10.1257/jep.15.4.143. EDN: https://elibrary.ru/DQPHDP

21. Korkusuz B. Volatility transmission in digital assets: Ethereum's rising influence // Journal of Risk and Financial Management. 2025. Vol. 18. No. 3. Art. 111. DOI:https://doi.org/10.3390/jrfm18030111. EDN: https://elibrary.ru/CNFOGA

22. Kyriazis N., Papadamou S., Tzeremes P., Corbet S. The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19 // The Quarterly Review of Economics and Finance. 2023. Vol. 89. P. 307-317. DOI:https://doi.org/10.1016/j.qref.2022.09.004. EDN: https://elibrary.ru/CEJDJM

23. Rahmani Cherati M., Haeri A., Ghannadpour S.F. Cryptocurrency direction forecasting using deep learning algorithms // Journal of Statistical Computation and Simulation. 2021. Vol. 91. No. 12. P. 2475-2489. DOI:https://doi.org/10.1080/00949655.2021.1899179. EDN: https://elibrary.ru/YAEFNV

24. Wang Y., Andreeva G., Martin-Barragan B. Machine learning approaches to forecasting cryptocurrency volatility: considering internal and external determinants // International Review of Financial Analysis. 2023. Vol. 90. Art. 102914. DOI:https://doi.org/10.1016/j.irfa.2023.102914. EDN: https://elibrary.ru/RREATL

25. Zhang J., Cai K., Wen J. A survey of deep learning applications in cryptocurrency // iScience. 2024. Vol. 27. No. 1. Art. 108509. DOI:https://doi.org/10.1016/j.isci.2023.108509. EDN: https://elibrary.ru/QAGJRM