MOSCOW ECONOMIC JOURNAL MOSCOW ECONOMIC JOURNAL Московский экономический журнал 2413-046X 117098 10.55186/2413046X_2026_11_2_27 zesdhx Мировая экономика World economy Мировая экономика MATHEMATICAL MODELING OF ABNORMAL CRYPTOCURRENCY RETURNS UNDER THE INFLUENCE OF INFORMATION EVENTS Математическое моделирование аномальных доходностей криптовалют под воздействием инфоповодов Сидоров Андрей Алексеевич Sidorov Andrey Alekseevich Бурцева Татьяна Александровна Burceva Tat'yana Aleksandrovna

доктор экономических наук;

doctor of economic sciences;

 Дарда Екатерина Сергеевна Darda Ekaterina Sergeevna

кандидат экономических наук;

candidate of economic sciences;

 Параскевопуло Ольга Ригасовна Paraskevopulo Olga Rigasovna

кандидат физико-математических наук;

candidate of physical and mathematical sciences;

 Ясский университет имени Александра Иоана Кузы Яссы Румыния Alexandru Ioan Cuza University of Iaşi Iași Romania Федеральное государственное бюджетное образовательное учреждение высшего образования «МИРЭА – Российский технологический университет» Москва Россия Federal State Budgetary Educational Institution of Higher Education “MIREA – Russian Technological University” Moscow Russian Federation Федеральное государственное бюджетное образовательное учреждение высшего образования «МИРЭА – Российский технологический университет» Москва Россия Federal State Budgetary Educational Institution of Higher Education “MIREA – Russian Technological University” Moscow Russian Federation Федеральное государственное бюджетное образовательное учреждение высшего образования «МИРЭА – Российский технологический университет» Москва Россия Federal state budget educational institution of higher education «MIREA — Russian technological university» Moscow Russian Federation 28 02 2026 28 02 2026 11 2 204 241 20 01 2026 27 02 2026 https://qje.su/en/nauka/article/117098/view

Целью исследования является анализ влияния публичных заявлений, регуляторных действий и значимых событий на динамику рынка криптовалют на примере Bitcoin, Ethereum, Ripple и Dogecoin. В работе рассматриваются поведенческие реакции рынка на положительные и отрицательные информационные поводы, оценивается уровень волатильности и устойчивости криптовалют, а также исследуются корреляционные связи между основными цифровыми активами. Гипотеза исследования заключается в том, что внешние информационные факторы и рыночные настроения оказывают существенное влияние на ценовую динамику криптовалют, причем значимую роль играет уровень информационной волатильности. Методологическую основу исследования составляют методы графического и статистического анализа, включая корреляционный анализ и методы анализа и прогнозирования временных рядов (метод Холта). Информационная база включает более 2700 ежедневных наблюдений за период 2017–2024 гг. В результате исследования выявлена высокая корреляция между реакциями Bitcoin и Ethereum (0,93), а также между Dogecoin и Ethereum (0,84), что свидетельствует о сходных тенденциях ценовых движений при воздействии информационных факторов. Установлено, что более волатильные криптовалюты, такие как Dogecoin и Ripple, демонстрируют более сильные и продолжительные реакции на позитивные новости, тогда как относительно стабильные активы быстрее возвращаются к равновесному состоянию. Негативные события, особенно связанные с регуляторными мерами и кризисами, оказывают более длительное и значительное влияние на рынок. Кроме того, выявлена тенденция роста устойчивости криптовалютного рынка по мере увеличения числа инвесторов. Практическая значимость исследования заключается в разработке рекомендаций для инвесторов по учету информационной волатильности при управлении рисками и использовании краткосрочных ценовых колебаний, вызванных информационными шоками. Полученные результаты подтверждают гипотезу о значительном влиянии информационных факторов на криптовалютный рынок и способствуют более глубокому пониманию механизмов формирования цен и поведения инвесторов.

The purpose of this study is to examine the impact of public statements, regulatory actions, and major events on the cryptocurrency market, focusing on Bitcoin, Ethereum, Ripple, and Dogecoin. The research analyzes behavioral patterns in market reactions to both positive and negative news, evaluates the volatility and stability of the cryptocurrency market, and investigates correlations among selected digital assets. The research hypothesis suggests that market sentiment and external information factors significantly influence cryptocurrency price movements, with the level of information volatility acting as a key determinant. The methodological framework of the study includes graphical and statistical analysis methods, such as correlation analysis and time series analysis and forecasting (Holt’s method). The data set consists of more than 2,700 daily observations covering the period from 2017 to 2024.The results reveal a strong correlation between the reactions of Bitcoin and Ethereum (0.93), as well as between Dogecoin and Ethereum (0.84), indicating similar price movement patterns in response to information triggers. Highly volatile assets such as Dogecoin and Ripple demonstrate stronger and more prolonged reactions to positive news, whereas relatively stable cryptocurrencies tend to stabilize more quickly. Negative events, particularly those related to regulation and crises, have a more persistent and significant negative impact on the market. The findings also indicate increasing market resilience associated with the growth in the number of investors.The practical significance of the research lies in providing recommendations for investors on accounting for information volatility in order to minimize risks and take advantage of speculative opportunities arising from information-driven price fluctuations. Overall, the study contributes to a deeper understanding of the structure of the cryptocurrency market and the key factors influencing its development.

 динамика рынка криптовалют рыночная капитализация ежедневная доходность криптовалют настроение новостей о криптовалютах эффект инфоповодов манипулирование рынком инфоволатильность криптовалют поведение инвесторов объем торгов cryptocurrency market dynamics market capitalization daily cryptocurrency returns cryptocurrency news sentiment informational event effect market manipulation cryptocurrency infovolatility investor behavior trading volume

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