financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2023-27-5-182-194finance-2406Research ArticleМЕЖДУНАРОДНЫЕ ФИНАНСЫINTERNATIONAL FINANCEТрансмиссия системного риска между банковскими системами стран Азиатско-Тихоокеанского региона и РоссииTransmission of systemic Risk between the banking systems of Asia-Pacific Countries and Russiahttps://orcid.org/0000-0001-9651-3158ДзюбаС. А.DzubaS. A.

Сергей Ануфриевич Дзюба —  доктор экономических наук

Владивосток

Sergey A. Dzuba — D r. Sci. (Econ.)

Vladivostok

dfirk@mail.ruhttps://orcid.org/0009-0002-4983-9387ТишковецВ. С.TishkovetzV. S.

Владислав Сергеевич Тишковец — бакалавр

Владивосток

Vladislav S. Tishkovetz —  Bachelor Sci.

Vladivostok

vtishkovetc@edu.hse.ruhttps://orcid.org/0000-0001-9107-3173ЩепелеваМ. А.ShchepelevaM. A.

Мария Александровна Щепелева —  кандидат экономических наук, доцент; научный сотрудник лаборатории «Новые тренды в международных финансах»

Москва

Maria A. Shchepeleva —  Cand. Sci. (Econ.), Assoc. Prof.; Research Associate, New Trends in International Finance Laboratory

Moscow

mshchepeleva@hse.ruДальневосточный федеральный университетРоссияFar Eastern Federal UniversityRussian FederationНациональный исследовательский университет «Высшая школа экономики»; МГИМО МИД РоссииРоссияNational Research University Higher School of Economics; MGIMO MFA of RussiaRussian Federation202323102023275182194Copyright © Дзюба С.А., Тишковец В.С., Щепелева М.А., 20232023Дзюба С.А., Тишковец В.С., Щепелева М.А.Dzuba S.A., Tishkovetz V.S., Shchepeleva M.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/2406

Предмет данного исследования —  механизмы передачи системного риска между финансовыми секторами разных стран.

Цель работы состоит в определении топологических характеристик сети, связывающей банковские системы стран Азиатско-Тихоокеанского региона (АТР) и России. Учитывая возрастающую роль стран этого региона на мировом финансов рынке, его подверженность кризисам может быть опасна для других стран.

Это определяет актуальность нашего исследования. Для построения сети мы использовали данные по показателям SRISK, отражающие потери капитала финансовых институтов в случае крупномасштабного кризиса. Сети были построены с использованием алгоритма NETS, предложенного Баригоцци и Браунлисом в 2019 г.

В основе этого метода лежит построение разреженных векторных авторегрессий, оцениваемых по методу LASSO. В результате применения алгоритма мы получаем две сети —  одновременных взаимосвязей и с использованием лагированных значений переменных. Сети были построены для временного периода 2005–2020 гг. и отдельно для подпериодов, включающих глобальный финансово-экономический кризис (2005–2013 гг.) и период пандемии COVID-19 (2014–2020 гг.).

Судя по полученным результатам, сети на всем рассматриваемом временном горизонте являлись достаточно уязвимыми по отношению к внешним рискам. К крупнейшим донорам шоков в этом регионе были отнесены Китай, Япония, Сингапур и Тайвань. Россия на горизонте 2014–2020 гг. выступала в качестве акцептора рисков.

Сделан вывод, что усиление/ослабление сотрудничества с крупнейшими экспортерами рисков в этом регионе для России может означать повышение/снижение вероятности заражения системным риском.

The subject of this research is systemic risk transmission between financial sectors in the international financial market.

The purpose of our paper is to determine topology characteristics for the network connecting banking systems in the Asia-Pacific region (APR) and Russia. Given the growing role of this region in the global financial market, its susceptibility to crises can be dangerous for other countries.

This determines the relevance of our study. To build the network, we used the SRISK indicators, which reflect capital losses in the financial institutions’ capital losses in case of a large-scale crisis. The networks were built with the use of the NETS algorithm, proposed by Barigozzi, M., & Brownlees, C. (2019).

This method is based on sparse vector autoregressions estimated by LASSO. As a result of the application the algorithm, we get two networks —simultaneous interconnections and using the values of the lagged variables. The networks were constructed for the 2005–2020 time period and separately for sub-periods including the global financial crisis (2005– 2013) and the COVID-19 pandemic period (2014–2020).

Based on the results obtained, the networks over the entire time period seem to be quite susceptible to external risks. China, Japan, Singapore and Taiwan are the largest shock donors in this region. Russia mainly accepts risks, generated by other countries, in the period 2014–2020. Strengthened/weakened cooperation with the largest risk exporters in this region will increase/decrease the likelihood of systemic risk transfer to the Russian financial sector.

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The authors declare that there are no conflicts of interest present.