financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2024-28-1-20-29finance-2675Research ArticleФИНАНСОВЫЕ РЫНКИFINANCIAL MARKETSПрогнозирование волатильности российского биржевого рынка акций в условиях международных экономических санкцийForecasting the Volatility of the Russian Stock Market in the Context of International Economic Sanctionshttps://orcid.org/0000-0002-9449-6013ГлебоваА. Г.GlebovaA. G.

Анна Геннадьевна Глебова — доктор экономических наук, профессор кафедры мировых финансов, факультет международных экономических отношений

Москва

Anna G. Glebova — Dr. Sci. (Econ.), Prof., Department of World Finance, Faculty of International Economic Relations

Moscow

nauka_rf@mail.ruhttps://orcid.org/0000-0002-1464-7329КовалеваА. А.KovalevaA. A.

Анжелика Анатольевна Ковалева — студентка факультета экономики и бизнеса

Москва

Anzhelika A. Kovaleva — student, Faculty of Economics and Business

Moscow

lika3107@mail.ruФинансовый университетРоссияFinancial UniversityRussian Federation2024010320242812029Copyright © Глебова А.Г., Ковалева А.А., 20242024Глебова А.Г., Ковалева А.А.Glebova A.G., Kovaleva A.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/2675

Статья посвящена исследованию тенденций развития российского биржевого рынка акций в условиях международных экономических санкций. Цель исследования заключается в составлении прогнозов волатильности российского биржевого рынка акций с применением сценарного подхода. Для расчетов использована информация Московской биржи. Авторами составлен прогноз волатильности биржевого рынка акций Российской Федерации. Основой прогнозных расчетов является динамика Индекса МосБиржи (IMOEX), взятого в качестве ключевого индикатора российского организованного рынка ценных бумаг, за период с июня 2013 по июль 2022 г. На основе базовой исторической динамики Индекса МосБиржи составлены негативный (международные экономические санкции ужесточаются) и позитивный (предполагает ослабление и/или снятие части санкций) сценарии развития фондового рынка Российской Федерации. Научную новизну составляет полученная авторами оценка сходимости прогноза волатильности при негативном и позитивном сценариях к определенному уровню волатильности в 2023 г. Результаты расчетов показали, что при разных сценариях развития ситуации волатильность при различных предполагаемых значениях Индекса МосБиржи стремится к одному и тому же значению, что позволило сделать новый и практически значимый вывод о том, что с течением времени экономика Российской Федерации стабилизируется вне зависимости от ужесточения или ослабления международных экономических санкций. Это может быть связано с реализацией в стране политики импортозамещения, формированием национального производства в большинстве сфер экономики и развитием внутреннего рынка. Выполненная авторами работа вносит вклад в развитие теоретической и прикладной экономической науки в части составления прогнозов развития фондового рынка и использования результатов прогнозирования для принятия экономически обоснованных решений.

The article is devoted to the study of trends in the development of the Russian stock market in the context of international economic sanctions. The purpose of the study is to make forecasts of the volatility of the Russian stock market using a scenario approach. Statistical data of the Moscow Stock Exchange were used for calculations. The authors have made a forecast of the volatility of the stock exchange market of the Russian Federation. The basis of the forecast calculations is the dynamics of the Moscow Exchange Index (IMOEX), taken as a key indicator of the Russian organized securities market, for the period from June 2013 to July 2022. Based on the basic historical dynamics of the Moscow Stock Exchange Index, negative (international economic sanctions are being tightened) and positive (implies the easing and/or lifting of some sanctions) scenarios for the development of the stock market of the Russian Federation are compiled. The scientific novelty is the authors’ assessment of the convergence of the volatility forecast under negative and positive scenarios to a certain level of volatility in 2023. The results of the calculations showed that under different scenarios of the situation, volatility tends to the same value at different assumed values of the Moscow Exchange Index, which allowed us to draw a new and practically significant conclusion that over time the economy of the Russian Federation stabilizes regardless of the tightening or easing of international economic sanctions — this may be due to the implementation of the country has a policy of import substitution, the formation of national production in most areas of the economy and the development of the domestic market. The work carried out by the authors contributes to the development of theoretical and applied economics in terms of making forecasts for the development of the stock market and using the results of forecasting to make economically sound decisions.

мировой фондовый рынокроссийский фондовый рынокволатильность фондового рынкамежду- народные экономические санкциимодели GARCH и E-GARCHИндекс МосБиржи (IMOEX)global stock marketRussian stock marketstock market volatilityinternational economic sanctionsGARCH and E-GARCH modelsMoscow Exchange Index (IMOEX)ReferencesKaranasos M., Yfant S., Hunter J. Emerging stock market volatility and economic fundamentals: The importance of US uncertainty spillovers, financial and health crises. Annals of Operations Research. 2022;313(2):1077–1116. DOI: 10.1007/s10479–021–04042-yKaranasos M., Yfant S., Hunter J. Emerging stock market volatility and economic fundamentals: The importance of US uncertainty spillovers, financial and health crises. Annals of Operations Research. 2022;313(2):1077–1116. DOI: 10.1007/s10479–021–04042-yEl Ouadghiri I., Erragragui E., Jaballah J., Peillex J.Institutional investor attention and stock market volatility and liquidity: International evidence. Applied Economics. 2022;54(42):4839–4854. DOI: 10.1080/00036846.2022.2036689El Ouadghiri I., Erragragui E., Jaballah J., Peillex J.Institutional investor attention and stock market volatility and liquidity: International evidence. Applied Economics. 2022;54(42):4839–4854. DOI: 10.1080/00036846.2022.2036689Ma Y., Wang Z., He F. How do economic policy uncertainties affect stock market volatility? Evidence from G7 countries. International Journal of Finance & Economics. 2022;27(2):2303–2325. DOI: 10.1002/ijfe.2274Ma Y., Wang Z., He F. How do economic policy uncertainties affect stock market volatility? Evidence from G7 countries. International Journal of Finance & Economics. 2022;27(2):2303–2325. DOI: 10.1002/ijfe.2274Bonfiglioli A., Crinò R., Gancia G. Economic uncertainty and structural reforms: Evidence from stock market volatility. Quantitative Economics. 2022;13(2):467–504. DOI: 10.3982/QE1551Bonfiglioli A., Crinò R., Gancia G. Economic uncertainty and structural reforms: Evidence from stock market volatility. Quantitative Economics. 2022;13(2):467–504. DOI: 10.3982/QE1551SalisuA.A., Demirer R., Gupta R. Financial turbulence, systemic risk and the predictability of stock market volatility. Global Finance Journal. 2022;52:100699. DOI: 10.1016/j.gfj.2022.100699SalisuA.A., Demirer R., Gupta R. Financial turbulence, systemic risk and the predictability of stock market volatility. Global Finance Journal. 2022;52:100699. DOI: 10.1016/j.gfj.2022.100699Lyócsa Š., Todorova N. Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks? International Journal of Forecasting. 2020;36(2):628–645. DOI: 10.1016/j.ijforecast.2019.08.002Lyócsa Š., Todorova N. Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks? International Journal of Forecasting. 2020;36(2):628–645. DOI: 10.1016/j.ijforecast.2019.08.002Lang Q., Wang J., Ma F., Huang D., Mohamed Ismail M.W.Is Baidu index really powerful to predict the Chinese stock market volatility? New evidence from the internet information. China Finance Review International. 2023;13(2);263– 284. DOI: 10.1108/CFRI-03–2021–0047Lang Q., Wang J., Ma F., Huang D., Mohamed Ismail M.W.Is Baidu index really powerful to predict the Chinese stock market volatility? New evidence from the internet information. China Finance Review International. 2023;13(2);263– 284. DOI: 10.1108/CFRI-03–2021–0047Lin Z. Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems. 2018;79(Pt.3):960–972. DOI: 10.1016/j.future.2017.08.033Lin Z. Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems. 2018;79(Pt.3):960–972. DOI: 10.1016/j.future.2017.08.033Фёдорова Е.А., Панкратов К.А. Моделирование волатильности фондового рынка в период кризиса. Финансовая аналитика: проблемы и решения. 2011;(37):21–30.Fedorova E.A., Pankratov K.A. Modeling of stock market volatility during the crisis. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience. 2011;(37):21–30. (In Russ.).Salisu A.A., Ogbonna A.E., Lasisi L., Olaniran A.Geopolitical risk and stock market volatility in emerging markets: A GARCH-MIDAS approach. The North American Journal of Economics and Finance. 2022;62:101755. DOI: 10.1016/j.najef.2022.101755Salisu A.A., Ogbonna A.E., Lasisi L., Olaniran A.Geopolitical risk and stock market volatility in emerging markets: A GARCH-MIDAS approach. The North American Journal of Economics and Finance. 2022;62:101755. DOI: 10.1016/j.najef.2022.101755Ma F., Wang J., Wahab M.I.M., Ma Y. Stock market volatility predictability in a data-rich world: A new insight. International Journal of Forecasting. 2023;39(4):1804–1819. DOI: 10.1016/j.ijforecast.2022.08.010Ma F., Wang J., Wahab M.I.M., Ma Y. Stock market volatility predictability in a data-rich world: A new insight. International Journal of Forecasting. 2023;39(4):1804–1819. DOI: 10.1016/j.ijforecast.2022.08.010Li X., Liang C., Ma F. Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model. Annals of Operations Research. 2022. DOI: 10.1007/s10479–022–04716–1Li X., Liang C., Ma F. Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model. Annals of Operations Research. 2022. DOI: 10.1007/s10479–022–04716–1Симонов П.М., Ахуньянова С.А.Сравнительный анализ методик AR-GARCH и p-адического прогнозирования волатильности финансового рынка. Вестник Пермского университета. Серия: Экономика. 2019;14(1):69– 92. DOI: 10.17072/1994–9960–2019–1–69–92Simonov P.M., Akhunyanova S.A. Comparative analysis of AR-GARCH and p-adic methods of the prediction of the financial market volatility. Vestnik Permskogo universiteta. Seriya: Ekonomika = Perm University Herald. Economy. 2019;14(1):69–92. (In Russ.). DOI: 10.17072/1994–9960–2019–1–69–92Кисилевский С.О. Моделирование процесса GARCH (1,1) для анализа волатильности при международной диверсификации портфеля акции. Сервис в России и за рубежом. 2011;(4):64–69.Kisilevskii S.O.Modeling of the GARCH (1,1) process for the analysis of volatility in the international diversification of the stock portfolio. Servis v Rossii i za rubezhom = Services in Russia and Abroad. 2011;(4):64–69. (In Russ.).Фёдорова Е.А., Назарова Ю.Н.Выявление факторов, влияющих на волатильность фондового рынка, с помощью коинтеграционного подхода. Экономический анализ: теория и практика. 2010;(3):17–24.Fedorova E.A., Nazarova Yu.N. Identification of factors affecting the volatility of the stock market using a cointegration approach. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice. 2010;(3):17– 24. (In Russ.).Seo M., Lee S., Kim G. Forecasting the volatility of stock market index using the hybrid models with Google domestic trends. Fluctuation and Noise Letters. 2019;18(01):1950006. DOI: 10.1142/S0219477519500068Seo M., Lee S., Kim G. Forecasting the volatility of stock market index using the hybrid models with Google domestic trends. Fluctuation and Noise Letters. 2019;18(01):1950006. DOI: 10.1142/S0219477519500068Ramos-Pérez E., Alonso-González P.J., Núñez-Velázquez J.J. Multi-transformer: A new neural network-based architecture for forecasting S&P volatility. Mathematics. 2021;9(15):1794. DOI: 10.3390/math9151794Ramos-Pérez E., Alonso-González P.J., Núñez-Velázquez J.J. Multi-transformer: A new neural network-based architecture for forecasting S&P volatility. Mathematics. 2021;9(15):1794. DOI: 10.3390/math9151794Di-Giorgi G., Salas R., Avaria R., et al. Volatility forecasting using deep recurrent neural networks as GARCH models. Computational Statistics. 2023;38(2). DOI: 10.1007/s00180–023–01349–1Di-Giorgi G., Salas R., Avaria R., et al. Volatility forecasting using deep recurrent neural networks as GARCH models. Computational Statistics. 2023;38(2). DOI: 10.1007/s00180–023–01349–1Chen W.-J., Yao J.-J., Shao Y.-H. Volatility forecasting using deep neural network with time-series feature embedding. Economic Research-Ekonomska Istraživanja. 2023;36(1):1377–1401. DOI: 10.1080/1331677X.2022.2089192Chen W.-J., Yao J.-J., Shao Y.-H. Volatility forecasting using deep neural network with time-series feature embedding. Economic Research-Ekonomska Istraživanja. 2023;36(1):1377–1401. DOI: 10.1080/1331677X.2022.2089192Valli S.V.S., Amruth Raj S., Jeevan Sail N., Lavanya K., Khadarunnisa. Stock market prediction using machine learning algorithms. Industrial Engineering Journal. 2023;52(4):786–796.Valli S.V.S., Amruth Raj S., Jeevan Sail N., Lavanya K., Khadarunnisa. Stock market prediction using machine learning algorithms. Industrial Engineering Journal. 2023;52(4):786–796.

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