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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">10.18288/1994-5124-2020-3-106-133</article-id><article-id custom-type="elpub" pub-id-type="custom">ecpolicy-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>Макроэкономика</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>Macroeconomics</subject></subj-group></article-categories><title-group><article-title>Анализ факторов банковских дефолтов 2013–2019 годов</article-title><trans-title-group xml:lang="en"><trans-title>Analysis of Bank Default Factors in 2013–2019</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>Зубарев</surname><given-names>А. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Zubarev</surname><given-names>A. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Андрей Витальевич Зубарев —  кандидат экономических наук,  старший научный сотрудник лаборатории математического моделирования экономических процессов Института прикладных экономических исследований</p><p>117517, Москва, пр. Вернадского, 82</p></bio><bio xml:lang="en"><p>Andrey V. Zubarev, Cand. Sci. (Econ.)</p><p>82, Vernadskogo pr., Moscow, 119571</p></bio><email xlink:type="simple">zubarev@ranepa.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Бекирова</surname><given-names>О. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Bekirova</surname><given-names>O. A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ольга Александровна Бекирова —  младший научный сотрудник  лаборатории математического моделирования экономических процессов Института прикладных экономических исследований</p><p>117517, Москва, пр. Вернадского, 82</p></bio><bio xml:lang="en"><p>Olga A. Bekirova</p><p>82, Vernadskogo pr., Moscow, 119571</p></bio><email xlink:type="simple">bekirova-oa@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>2020</year></pub-date><pub-date pub-type="epub"><day>28</day><month>06</month><year>2020</year></pub-date><volume>15</volume><issue>3</issue><fpage>106</fpage><lpage>133</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Зубарев А.В., Бекирова О.А., 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder xml:lang="ru">Зубарев А.В., Бекирова О.А.</copyright-holder><copyright-holder xml:lang="en">Zubarev A.V., Bekirova O.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/732">https://www.ecpolicy.ru/jour/article/view/732</self-uri><abstract><p>С приходом на пост главы Банка России в 2013 году Э. Набиуллиной политика регулятора резко изменилась: был взят курс на сокращение числа проблемных кредитных организаций. Кризис 2014–2015 годов, вызванный снижением мировых цен на нефть и экономическими санкциями, оказал влияние на банковский сектор посредством реализовавшихся валютных рисков и роста просроченной задолженности по кредитам. В работе исследуются факторы, которые статистически значимо коррелировали с вероятностью банковских дефолтов в России в период с III квартала 2013 года по I квартал 2019-го. Сделана попытка понять, определяются ли действия Банка России динамикой показателей банковских балансов. Основным эконометрическим инструментом являются бинарные логистические регрессии. Полученные результаты позволяют сделать выводы, что вероятность банковского дефолта повышают факторы, характеризующие вовлеченность кредитной организации в классическую банковскую деятельность. Явным индикатором дефолта являются резервы на возможные потери. В работе сконструирована отдельная переменная, характеризующая степень создания банком ликвидности в экономике. Согласно полученным оценкам, высокие уровни создания ликвидности банком могут повышать риск несостоятельности. Оценены также модели отдельно для острой фазы кризиса и для последующего периода. Важным выводом здесь является значимость показателя излишнего создания ликвидности лишь в острой фазе кризиса, что может объясняться большей чувствительностью к дополнительным рискам в кризисный момент. Аналогичные модели были оценены для выборки из 150 крупных банков, которая является более однородной и интересует регулятора в первую очередь. На этой выборке ряд переменных, в том числе уровень создания ликвидности, оказался незначимым, однако высокие значения резервов на возможные потери по-прежнему увеличивают вероятность дефолта. В качестве альтернативной спецификации была рассмотрена логистическая панельная регрессия.</p></abstract><trans-abstract xml:lang="en"><p>This paper studies bank defaults in the Russian Federation in recent years. Firstly, the Central Bank of Russia tightened prudential regulation in 2013. Secondly, a decrease in oil prices and economic sanctions resulted in a crisis in 2014–2015 with a huge depreciation of the national currency, which influenced the Russian banking sector substantially. Almost half of banks in Russia have been closed in the last 6 years. Through binary logistic models of bank defaults based on data for Q3 2013 through Q1 2019, the paper reveals the key factors which had an influence on the sustainability of Russian banks. The main result is that involvement in classical banking exposes banks to default risks. Excessive reserves appeared to be an important indicator of default as well. A special measure of liquidity creation was constructed. We found that high levels of liquidity creation increased the probability of bank failure. It is also worth mentioning that excessive liquidity creation put higher risks on a given bank in the crisis period. We can conclude that regulatory authorities should pay attention to high liquidity creators, especially in the group of small and medium-sized banks. We also found some evidence of an improvement in prudential regulation by the Bank of Russia. Separate models were estimated for the sample of 150 larger banks, which is more homogeneous and is of primary interest for the regulator. A number of variables, including the level of liquidity creation, turned out to be insignificant; however, high reserve values for possible losses still increase the probability of default to a large extent.</p><p>Logistic panel regressions were also considered as an alternative specification.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>банковский дефолт</kwd><kwd>логистическая регрессия</kwd><kwd>ЦБ РФ</kwd></kwd-group><kwd-group xml:lang="en"><kwd>bank default</kwd><kwd>logistic regression</kwd><kwd>Central Bank</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Статья подготовлена в рамках выполнения научно-исследовательской работы государственного задания РАНХиГС.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Дробышевский С. М. 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