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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">finance</journal-id><journal-title-group><journal-title xml:lang="ru">Финансы: теория и практика/Finance: Theory and Practice</journal-title><trans-title-group xml:lang="en"><trans-title>Finance: Theory and Practice</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2587-5671</issn><issn pub-type="epub">2587-7089</issn><publisher><publisher-name>Financial University under The Government of Russian Federation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26794/2587-5671-2018-22-4-38-51</article-id><article-id custom-type="elpub" pub-id-type="custom">finance-732</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>СОВРЕМЕННЫЕ МЕТОДЫ ИССЛЕДОВАНИЯ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MODERN RESEARCH METHODS</subject></subj-group></article-categories><title-group><article-title>МЕТОДИЧЕСКИЕ ПОДХОДЫ К ПРОГНОЗИРОВАНИЮ ДИНАМИКИ КУРСА КРИПТОВАЛЮТ С ПРИМЕНЕНИЕМ ИНСТРУМЕНТОВ СТОХАСТИЧЕСКОГО АНАЛИЗА (НА ПРИМЕРЕ БИТКОИНА)</article-title><trans-title-group xml:lang="en"><trans-title>METHODOLOGICAL APPROACHES TO FORECASTING DYNAMICS OF CRYPTOCURRENCIES EXCHANGE RATE USING STOCHASTIC ANALYSIS TOOLS (ON THE EXAMPLE OF BITCOIN)</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3708-8184</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Сафиуллин</surname><given-names>М. Р.</given-names></name><name name-style="western" xml:lang="en"><surname>Safiullin</surname><given-names>M. R.</given-names></name></name-alternatives><bio xml:lang="ru"><p>доктор экономических наук, профессор, проректор по вопросам экономического и стратегического развития</p></bio><bio xml:lang="en"><p>Dr. Sci. (Econ.), Professor, Vice-rector for economic and strategic development</p></bio><email xlink:type="simple">Marat.Safiullin@tatar.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1262-5588</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Абдукаева</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Abdukaeva</surname><given-names>A. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>старший научный сотрудник, Центр перспективных экономических исследований Академии наук Республики Татарстан</p></bio><bio xml:lang="en"><p>senior researcher</p></bio><email xlink:type="simple">Aliya.Abdukaeva@tatar.ru</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0763-6453</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ельшин</surname><given-names>Л. А.</given-names></name><name name-style="western" xml:lang="en"><surname>El’shin</surname><given-names>L. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>доктор экономических наук, заведующий отделом макроисследований и экономики роста</p></bio><bio xml:lang="en"><p>Dr. Sci. (Econ.), head of the Department of macro-studies and growth economics of the Center for advanced economic research of Academy of Sciences of the Republic of Tatarstan</p></bio><email xlink:type="simple">Leonid.Elshin@tatar.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Казанский (Приволжский) федеральный университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Kazan (Privolzhsky) Federal university</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Центр перспективных экономических исследований Академии наук Республики Татарстан</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Center for advanced economic research of Academy of Sciences of the Republic of Tatarstan</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2018</year></pub-date><pub-date pub-type="epub"><day>02</day><month>10</month><year>2018</year></pub-date><volume>22</volume><issue>4</issue><fpage>38</fpage><lpage>51</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Сафиуллин М.Р., Абдукаева А.А., Ельшин Л.А., 2018</copyright-statement><copyright-year>2018</copyright-year><copyright-holder xml:lang="ru">Сафиуллин М.Р., Абдукаева А.А., Ельшин Л.А.</copyright-holder><copyright-holder xml:lang="en">Safiullin M.R., Abdukaeva A.A., El’shin L.A.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://financetp.fa.ru/jour/article/view/732">https://financetp.fa.ru/jour/article/view/732</self-uri><abstract><p>Ускоренные темпы развития рынка криптовалюты и его интеграция в систему хозяйственных, операционных, финансовых и других процессов определяют необходимость комплексного изучения данного явления. Особую актуальность этому придает то, что на государственном уровне в последние месяцы активизировались обсуждения относительно перспектив легализации рынка криптовалюты и возможностей использования его инструментов в хозяйственной деятельности экономических агентов. Несмотря на порой полярные взгляды и подходы, сформировавшиеся на текущий момент среди российских экспертов относительно решения данного вопроса, развитие крипторынка происходит крайне высокими темпами вне зависимости от его регулирования. Это обусловливает и актуализирует проведение научных изысканий в области оценки перспектив развития данного рынка, формирующих предмет настоящего исследования с целью предсказания возможных эффектов и рисков для национальной экономической системы. Цель статьи — разработка инструментария, направленного на решение вопросов в части моделирования и прогнозирования волатильности рынка криптовалюты на основе «предвидения» перспективных колебаний стоимости «цифровых денег» с использованием специальных моделей авторегрессии (ARMA, ARIMA). Исследование базируется на использовании класса параметрических моделей, позволяющих описывать как стационарные, так и нестационарные временные ряды и на этой основе разрабатывать систему прогностических оценок относительно перспектив дальнейшего развития исследуемого ряда.При помощи полученной модели ARIMA, оценивающей параметры анализируемого ряда, характеризующего курс криптовалюты, разработана система прогностических оценок на краткосрочный период.Доказано, что использование подобного рода моделей с высоким уровнем достоверности предсказывает будущие корректировки на исследуемом рынке, что обусловливает высокий уровень перспективности их использования при моделировании будущих параметров развития рынка криптовалюты. Это создает основу для выработки механизмов адаптации хозяйствующих субъектов к формирующимся корректировкам ценовых индексов «цифровых денег».</p></abstract><trans-abstract xml:lang="en"><p>The accelerated pace of development of the cryptocurrency market and its integration into the system of economic, operational, financial and other processes determines the need for a comprehensive study of this phenomenon. This is particularly relevant because in recent months, at the state level have intensified discussions on the prospects of the legalization of the cryptocurrency market and the possibility of using its tools in the economic activities of economic agents. Despite the sometimes polar views and approaches at the moment among Russian experts regarding the solution to this issue, the development of the crypto-currencies market is extremely high, regardless of its regulation. This determines and actualizes the scientific research in the field of evaluation of the prospects of development of this market, forming the subject of this study in order to predict the possible effects and risks for the national economic system. The purpose of the article is the development of tools of modelling and forecasting the volatility of the cryptocurrency market on the basis of “foreseeing” fluctuations in the value of “digital money” using special models of autoregression (ARMA, ARIMA). The study was based on the application of a class of parametric models. It allowed describing both stationary and non-stationary time series and on this basis to develop a system of prognostic estimates for the prospects of further development of the series under study. With the help of our ARIMA model, which evaluates the parameters of the analyzed time series of the cryptocurrency exchange rate, we developed a system of prognostic assessments for the short term. The authors proved that the application of such models with a high level of reliability predicts future adjustments in the market under study. It leads to a high level of prospects for their use in modelling future parameters of the cryptocurrency market development. This creates a basis for a business to develop adaptive mechanisms for to emerging price index adjustments of “digital money”.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>рынок криптовалюты</kwd><kwd>биткоин</kwd><kwd>стохастический анализ</kwd><kwd>прогнозирование</kwd><kwd>модели авторегрессии</kwd></kwd-group><kwd-group xml:lang="en"><kwd>cryptocurrency market</kwd><kwd>bitcoin</kwd><kwd>stochastic analysis</kwd><kwd>forecasting</kwd><kwd>autoregression models</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Cocco L., Concas G., Marchesi M. 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