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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">ecpolicy</journal-id><journal-title-group><journal-title xml:lang="ru">Экономическая политика</journal-title><trans-title-group xml:lang="en"><trans-title>Economic Policy</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1994-5124</issn><issn pub-type="epub">2411-2658</issn><publisher><publisher-name>Economic Policy</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.18288/1994-5124-2022-6-140-165</article-id><article-id custom-type="elpub" pub-id-type="custom">ecpolicy-62</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>ФИНАНСОВЫЕ РЫНКИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>FINANCIAL MARKETS</subject></subj-group></article-categories><title-group><article-title>Эффективность рынка криптовалют после пандемии COVID-19</article-title><trans-title-group xml:lang="en"><trans-title>Efficiency of the Cryptocurrency Market After the COVID-19 Pandemic</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Носков</surname><given-names>Н. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Noskov</surname><given-names>N. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Никита Александрович Носков, бакалавр отделения экономики ЭМИТ</p><p>117517, Москва, пр. Вернадского, 82</p></bio><bio xml:lang="en"><p>Nikita A. Noskov, Bachelor Student, Institute of Economics, Mathematics and Information Technology</p><p>82, Vernadskogo pr., Moscow, 119571</p></bio><email xlink:type="simple">niknoom@yandex.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/0000-0002-2149-3946</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Шилов</surname><given-names>К. Д.</given-names></name><name name-style="western" xml:lang="en"><surname>Shilov</surname><given-names>K. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кирилл Дмитриевич Шилов, научный сотрудник лаборатории прикладных макроэкономических исследований центра математического моделирования экономических процессов Института прикладных экономических исследований</p><p>117517, Москва, пр. Вернадского, 82</p></bio><bio xml:lang="en"><p>Kirill A. Shilov, Research Fellow, Laboratory of Applied Macroeconomics at the Center for Mathematical Modeling of Economic Processes of the Institute of Applied Economic Research</p><p>82, Vernadskogo pr., Moscow, 119571</p></bio><email xlink:type="simple">shilov-kd@ranepa.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>РАНХиГС</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Russian Presidential Academy of National Economy and Public Administration</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>28</day><month>12</month><year>2022</year></pub-date><volume>17</volume><issue>6</issue><fpage>140</fpage><lpage>165</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Носков Н.А., Шилов К.Д., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Носков Н.А., Шилов К.Д.</copyright-holder><copyright-holder xml:lang="en">Noskov N.A., Shilov K.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://www.ecpolicy.ru/jour/article/view/62">https://www.ecpolicy.ru/jour/article/view/62</self-uri><abstract><p>Целью настоящей работы является проверка слабой формы гипотезы эффективного рынка для самых высококапитализированных криптовалют из различных категорий: средства платежа, платежные системы, блокчейн-платформы и utility-токены — в период после марта 2020 года. Для проверки гипотезы в работе применяются традиционные для этой цели тесты на автокорреляцию и тесты серий. Также предпринята попытка построения прибыльных торговых стратегий, использующих информацию с традиционных рынков для принятия решения об открытии позиций в разных криптовалютах — в случае удачи это стало бы свидетельством против гипотезы об эффективности. Результаты проведенных статистических тестов демонстрируют повышение эффективности рынка криптовалют, а следовательно, падение прибыльности спекулятивной торговли на основе прошлых цен (технический анализ). Результаты торговых симуляций показали, что ввиду повышенной чувствительности криптовалютного рынка к динамике традиционных рынков в период после марта 2020 года появилась возможность построения торговых стратегий, учитывающих рыночную информацию и способных генерировать значительную избыточную по сравнению со стратегией «купить и держать» доходность в период спада на рынках цифровых активов (2021–2022) для некоторых криптовалют (Bitcoin, Dash, Zcash, Lumen). Таким образом, можно сделать вывод о постепенном повышении эффективности рынка криптовалют в части возможностей торговли на основе технического анализа. При этом благодаря росту степени сонаправленности с традиционным рынком можно говорить о появлении неэффективности в части предсказания доходностей криптовалют на основе внешней информации, которая в других исследованиях, выполненных на более ранних периодах, оказывалась нерелевантной.</p></abstract><trans-abstract xml:lang="en"><p>The aim of this study is to test the weak form of the efficient market hypothesis for the most highly capitalized cryptocurrencies in various categories (means of payment, payment systems, blockchain platforms, and utility tokens) after March 2020. The study uses standard statistical tools – autocorrelation tests and series tests – to evaluate the hypothesis, which is then tested in another way by attempting to arrive at profitable trading strategies using information from traditional markets to decide whether to open positions in various cryptocurrencies. The results of the statistical tests conducted demonstrate an increase in the efficiency of the cryptocurrency market and a consequent reduction in the profitability of speculative trading based on past prices (technical analysis). The trading simulations show that the increased sensitivity of the cryptocurrency market to the dynamics of traditional markets after March 2020 made it possible to find trading strategies that take into account market information and are able to generate significant excess returns compared to a “buy and hold” strategy during the downturn in the digital asset markets (2021–2022) for certain cryptocurrencies (Bitcoin, Dash, Zcash, Lumen). This supports the conclusion that the efficiency of the cryptocurrency market is gradually increasing in terms of identifying trading opportunities through technical analysis. However, the increase in comovement with the traditional market would suggest that it is inefficient to predict the returns of cryptocurrencies based on external information, which turned out to be irrelevant in other studies that examined earlier periods.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>криптовалюты</kwd><kwd>гипотеза эффективного рынка</kwd><kwd>торговые симуляции</kwd></kwd-group><kwd-group xml:lang="en"><kwd>cryptocurrency</kwd><kwd>efficient market hypothesis</kwd><kwd>trading simulations</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">Криптовалюты: Тренды, риски, меры. М.: Банк России, 2022.</mixed-citation><mixed-citation xml:lang="en">Kriptovalyuty: Trendy, riski, mery [Cryptocurrencies: Trends, Risks and Regulation]. Moscow, Bank of Russia, 2022. 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