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Corrected Triple Correction Method, CNN and Transfer Learning for Prediction the Realized Volatility of Bitcoin and E-Mini S&P500
Lobachevskii Journal of Mathematics. 2024. Vol. 45. No. 3. P. 1194–1206.
Keywords: forecastingrealized volatilitybitcoinE-mini S&P 500 cryptocurrencies corrected triple correction method
Publication based on the results of:
Kuslyaikin A., Финансы и бизнес 2026 Т. 22 № 1 С. 62–78
Does halving matter for cryptocurrency trade dynamics? This question is in focus of current paper, providing the first comprehensive research on the issue. Results of event study indicate clearly that both cryptocurrency trading volumes and returns are highly affected by halving events, and effects for returns are less notable than for volumes. Prevailing pattern is ...
Added: May 14, 2026
Lysenok N., Фундаментальная и прикладная математика 2026 Т. 26 № 3 С. 33–42
This study examines the impact of realized volatility forecasts on the performance of active trading strategies in the Russian equity market. Using a sample of 17 liquid stocks over the period 2014–2026, a hybrid forecasting model is developed that combines HAR-J with gradient boosting; its superiority over the baseline HAR-J specification is confirmed by the ...
Added: April 17, 2026
Kopnova E., Журавлева К. А., Коряков И. В. et al., Журнал Белорусского государственного университета. Экономика 2025 № 1 С. 36–46
he article examines the prospects for economic cooperation between Russia and Belarus within the framework of the EAEU, SCO and BRICS integration associations, with an emphasis on the impact of sanctions and their consequences for the economic growth of the countries. The study includes the use of econometric modeling methods to evaluate the forecast of ...
Added: December 11, 2025
Volkova O. N., Финансы: теория и практика 2025 Т. 29 № 5 С. 21–33
Cryptocurrencies are a type of financial instrument that has been widely used by financial market participants since
the early 2010s. Despite their growing popularity, their status within financial systems across different countries
remains a topic of ongoing discussion. There is still no consensus on how to best understand the economic nature of
these digital assets. This paper uses ...
Added: November 5, 2025
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2025. P. 987–991.
Industry 4.0 concept focuses on sustainability problem that requires to control air emissions, especially for harmful substances like H2S, and reduction their impact on nature by using environmental monitoring and sources identification systems. This task requires solving inverse problem of dispersion models, which should establish complex mathematical dependences between the sensor data, the location and ...
Added: November 4, 2025
Kosov M., Вестник экономической безопасности 2024 № 2 С. 185–191
The development of modern payment systems is associated with the emergence of so-called electronic or digital money. Under these conditions, cryptocurrencies are emerging and rapidly spreading as a means of payment. Currently, transactions with them are carried out on exchanges on many resources on the Internet. This trend required states to address issues of regulating ...
Added: October 22, 2025
Smirnov S. V., Вопросы экономики 2025 № 10 С. 131–154
The paper summarizes machine-learning (ML) methods most relevant to macroeconomics and assesses their performance in forecasting and nowcasting key macro indicators. Despite rapid methodological progress and a surge of publications over the past 25 years, gains in forecast accuracy with traditional statistical (economic, financial, and survey) data remain modest. ML models often outperform naïve and ...
Added: October 12, 2025
Kychkin A., Chernitsin I., Vikentyeva O., , in: 2025 International Russian Automation Conference (RusAutoCon).: IEEE, 2025. P. 1046–1050.
This paper presents system architecture of the automated AI-driven predictive assessment of the ecological state of the air in sanitary protection zones around industrial facilities. The developed solution implements automated pre-processing of data presented in the form of time series using machine learning methods and the construction of short-term forecasts of pollutant concentrations for the ...
Added: October 6, 2025
Чан Ф. Т., Староверова О. В., Kosov M., Аудиторские ведомости 2025 № 2 С. 129–134
This article is devoted to the study of factors affecting enterprise cash flow management. Methods of analysis and synthesis of peer-reviewed scientific sources on this issue are applied. As a result of the research, two groups of factors have been identified and systematized: objective factors (state economic policy, customer influence) and subjective factors (competence of ...
Added: September 22, 2025
Teterin M., Экономическая политика 2025 Т. 20 № 4 С. 82–117
Bitcoin, Ether, Litecoin and XRP are among the largest cryptocurrencies and together have a market capitalization that constitutes a substantial portion of the digital asset market. At the same time, the cryptocurrency market differs from that of traditional financial assets because it has greater price volatility, which makes constructing more accurate forecasts of daily volatility ...
Added: September 2, 2025
Karacharovskiy V., Социологические исследования 2025 № 6 С. 3–14
The article evaluates the possibility of forecasting mass social attitudes based on the interest of the population for the eras in the development of the country. Historical eras and their symbols are considered as comparison standards, temporal analogues of reference groups that provide the mass consciousness with samples of “reserve” standards of living and standards ...
Added: August 12, 2025
К прогнозированию вероятности невооруженной революционной дестабилизации методами машинного обучения
Медведев И. А., Korotayev A., Социология власти 2025 Т. 37 № 2 С. 108–141
The authors provide a broad overview of the main applications for machine learning methods in political sociology. They describe history of the transition from simple regression models to complex machine learning models. The reasons for and benefits of this transition are discussed. The authors identify the main uses of machine learning models in related disciplines and ...
Added: August 1, 2025
Teterin M., Peresetsky A., Applied Econometrics 2025 Vol. 77 P. 74–90
Bitcoin and Ethereum are the two world’s largest cryptocurrencies. Their market capitalizations have recently peaked, making them more attractive to portfolio investors. As the cryptocurrency market is known for its high volatility nature, government institutions are also interested in this segment of the financial market for more comprehensive regulation. Volatility forecasting is a crucial part ...
Added: April 22, 2025
Tatyana A. Alexeeva, Kuznetsov N., Mokaev T. et al., Chaos, Solitons and Fractals 2025 Vol. 196 Article 116371
Irregular dynamics (especially chaotic) is often undesirable in economics because it presents challenges for predicting and controlling the behavior of economic agents. In this paper, we used an overlapping generations (OLG) model with a control function in the form of government spending as an example, to demonstrate an effective approach to forecasting and regulating chaotic ...
Added: April 20, 2025
Pshichenko D., Znanstvena misel 2024 No. 96 P. 38–42
The article analyzes the application of artificial intelligence (AI) models for forecasting market volatility (MV). Examples of algorithms such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, and regression methods are studied, demonstrating their effectiveness in processing time series and identifying complex data patterns. The importance of integrating machine learning (ML), as a ...
Added: March 10, 2025
Габов М. А., Bukina T. V., Kashin D., Журнал Новой экономической ассоциации 2025 № 4(69) С. 87–117
The study aims to compare approaches to forecasting the monthly level of consumer price index (CPI y/y) in the regions of the Volga Federal District using time series models and machine learning methods. This study attempts to select the most appropriate and efficient models for predicting the regional general price level index. The paper also ...
Added: February 22, 2025
Teterin M., Peresetsky A., The Journal of the New Economic Association 2024 Vol. 4 P. 118–135
Since the introduction of Bitcoin in 2008, the size of the cryptocurrency market is becoming increasingly important for investors. Thus, the forecast of cryptocurrency price volatility is of particular interest to portfolio investors, as they are interested in accurately estimating the standard deviation of their portfolios to calculate the Value-at- Risk (VaR) as a risk ...
Added: January 21, 2025
Б.В. Уткин, Н.Н. Грачёв, Журнал Сибирского федерального университета. Серия: Техника и технологии 2024 Т. 17 № 8 С. 1077–1088
The following article presents an analysis of electromagnetic interference formation caused by the secondary electromagnetic re- emissions formed by radio electronic devices. Based on the results of the conducted research, a method for predicting the spectral characteristics of contact interference to a radio receiver is proposed. The methodology for predicting the spectral composition of contact ...
Added: December 28, 2024
Lola I. S., Asoskov D., Russian Journal of Economics 2024 No. 10(4) P. 351–364
This paper investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset ...
Added: December 10, 2024
Lola I. S., Asoskov D., / NRU Higher School of Economics. Series WP BRP "Science, Technology and Innovation". 2024. No. 128/STI/2024.
This paper investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset ...
Added: November 2, 2024