- PII
- S30346177S0424738825030034-1
- DOI
- 10.7868/S3034617725030034
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume 61 / Issue number 3
- Pages
- 28-42
- Abstract
- The article examines the transmission of financial contagion between global stock indices, such as the (USA), (European countries), (China) and (Russia), during the pandemic and new anti-Russian sanctions. ARMA–TGARCH models were used to cleanse the indices’ returns from their own trends and volatility. Shock periods were identified based on the 90th percentile of conditional return volatility. The construction of Gaussian and Student copulas for shock and relatively calm periods made it possible to estimate the change in dependencies between index returns taking into account their marginal distributions. The study confirmed financial contagion between all indices (except for the S&P 500 — STOXX 600 pair) during the acute phase of the pandemic, as well as contagion between the European countries index, on the one hand, and the American and Chinese indices, on the other hand, during the period of new sanctions. Calculating the dependencies for the upper and lower tails of the distribution revealed a greater joint reaction of markets to negative shocks than to positive shocks, and demonstrated the dominance of the wealth channel in contagion compared to the portfolio rebalancing channel. The study develops new progressive methods for analyzing the consequences of global risks for the functioning of national financial systems and assessing the effects of financial contagion. It can be useful for investors to manage portfolios and hedge risks, and for governments to pursue effective financial stabilization policies during periods of global shocks.
- Keywords
- финансовое заражение фондовые индексы метод копул модель ARMA модель TGARCH
- Date of publication
- 18.12.2025
- Year of publication
- 2025
- Number of purchasers
- 0
- Views
- 38
References
- 1. Артамонов Н. В., Ивин Е. А., Курбанкий А. Н., Фанташини Д. (2021). . Вологда: ВолНЦ РАН. 134 с. Режим доступа: https://clck.ru/3AtL3H
- 2. Artamonov N. V., Ivin E. A., Kurbatskii A. N., Fantazzini D. (2021). Introduction to time series analysis: A tutorial for universities. Vologda: VolSC RAN. 134 p. Available at: https://clck.ru/3AtL3H (in Russian).
- 3. Благовещенский Ю. Н. (2012). Основные элементы теории копул // . Т. 26. № 2. С. 113–130.
- 4. Blagoveschensky Y. N. (2012). Basics of copula’s theory. Applied Econometrics, 26 (2), 113–130 (in Russian).
- 5. Бусыгин С. В., Шарыпов Р. О. (2019). Применение копул в многомерном анализе обменных курсов на примере развивающихся стран Европы // . Т. 19. № 3. С. 58–72. DOI: 10.25205/2542-0429-2019-19-3-58-72
- 6. Busygin S. V., Sharypov R. O. (2019). Copula approach in multivariate exchange rate analysis of developing countries in Eastern Europe. World of Economics and Management, 19 (3), 58–72. DOI: 10.25205/2542-0429-2019-19-3-58-72 (in Russian).
- 7. Кендьсь А. М., Труш Н. Н. (2024). Применение моделей копул в анализе акций фондового рынка // . Т. 21. № 2. С. 24–35. DOI: 10.37661/1816-0301-2024-21-2-24-35
- 8. Kendys A. M., Troush M. M. (2024). Application of copula models in stock market analysis. Informatics, 21 (2), 24–35. DOI: 10.37661/1816-0301-2024-21-2-24-35 (in Russian).
- 9. Пеникас Г. И. (2010a). Модели «копула» в приложении к задачам финансов // . № 7 (7). С. 24–44.
- 10. Penikas H. I. (2010). Financial applications of copula-models. The Journal of the New Economic Association, 7 (7), 24–44 (in Russian).
- 11. Пеникас Г. И. (2010б). Модели «копула» в управлении валютным риском банка // . № 1 (17). С. 62–87.
- 12. Penikas H. I. (2010). Copula models in bank currency risk management. Applied Econometrics, 1 (17), 62–87 (in Russian).
- 13. Пеникас Г. И., Симакова В. Б. (2009). Управление процентным риском на основе копулы-GARCH моделей // . № 1 (13). С. 3–36.
- 14. Penikas H. I., Simakova V. B. (2012). Interest rate risk management based on copula-GARCH models. Applied Econometrics, 1 (13), 3–36 (in Russian).
- 15. Правдухин М. М. (2019). Применение копула-функций в управлении риском портфеля акций // . № 15 (1). С. 33–58. DOI: 10.31085/1814-4802-2019-15-1-33-58
- 16. Pravdukhin M. M. (2019). Application of copula-functions in portfolio risk management. Finance and Business, 15 (1), 33–58. DOI: 10.31085/1814-4802-2019-15-1-33-58 (in Russian).
- 17. Фантаццини Д. (2011a). Моделирование многомерных распределений с использованием копула-функций. Часть I // . № 22 (2). С. 98–134.
- 18. Fantazzini D. (2011a). Analysis of multidimensional probability distributions with copula functions. Part I. Applied Econometrics, 22 (2), 98–134 (in Russian).
- 19. Фантаццини Д. (2011б). Моделирование многомерных распределений с использованием копула-функций. Часть II // . № 23 (3). С. 98–132.
- 20. Fantazzini D. (2011b). Analysis of multidimensional probability distributions with copula functions. Part II. Applied Econometrics, 23 (3), 98–132 (in Russian).
- 21. Фантаццини Д. (2011в). Моделирование многомерных распределений с использованием копула-функций. Часть III // . № 24 (4). С. 100–130.
- 22. Fantazzini D. (2011c). Analysis of multidimensional probability distributions with copula functions. Part II. Applied Econometrics, 24 (4), 100–130 (in Russian).
- 23. Algaralleh H., Canepa A. (2021). Evidence of stock market contagion during the COVID-19 pandemic: A wavelet-copula-GARCH approach. , 14 (7), 329. DOI: 10.3390/jrfm14070329
- 24. Benkraiem R., Garfatta R., Lakhal F., Zorgati I. (2022). Financial contagion intensity during the COVID-19 outbreak: A copula approach. , 81, 102136. DOI: 10.1016/j.irfa.2022.102136
- 25. Bollerslev T. (1986). Generalized autoregressive conditional heteroscedasticity. , 31 (3), 307–327. DOI: 10.1016/0304-4076 (86)90063-1
- 26. Chen S., Li Q., Wang Q., Zhang Y.Y. (2023). Multivariate models of commodity futures markets: A dynamic copula approach. , 64, 3037–3057. DOI: 10.1007/s00181-023-02373-2
- 27. Cubillos-Rocha J.S., Gomez-Gonzalez J.E., Melo-Velandia L.F. (2019). Detecting exchange rate contagion using copula functions. , 47, 13–22. DOI: 10.1016/j.najef.2018.12.001
- 28. Ding H., Kim H.-G., Park S.Y. (2016). Crude oil and stock markets: Causal relationships in tails? , 59, 58–69. DOI: 10.1016/j.eneco.2016.07.013
- 29. Engle R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. , 50 (4), 987–1007. DOI: 10.2307/1912773
- 30. Fenech J.-P., Vosgha H. (2019). Oil price and Gulf Corporation Council stock indices: New evidence from time-varying copula models. , 77, 81–91. DOI: 10.1016/j.econmod.2018.09.009
- 31. Forbes K., Rigobon R. (2002). No contagion, only interdependence: Measuring stock market co-movements. , 57 (5), 2223–2261. Available at: http://www.jstor.org/stable/3094510
- 32. Fry R., Martin V.L., Tang C. (2010). A new class of tests of contagion with applications. , 28 (3), 423–437. DOI: 10.1198/jbes.2010.06060
- 33. Fry-McKibbin R., Hsiao C.Y.L. (2018). Extremal dependence tests for contagion. , 37 (6), 626–649. DOI: 10.1080/07474938.2015.1122270
- 34. Gomez-Gonzalez J.E., Rojas-Espinosa W. (2019). Detecting contagion in Asian exchange rate markets using asymmetric DCC-GARCH and R-vine copulas. , 43 (3–4), 100717. DOI: 10.1016/j.ecosys.2019.100717
- 35. Jayech S. (2016). The contagion channels of July-August-2011 stock market crash: A DAG-copula based approach. , 249 (2), 631–646. DOI: 10.1016/j.ejor.2015.08.061
- 36. Lu Y., Xiao D., Zheng Z. (2023). Assessing stock market contagion and complex dynamic risk spillovers during COVID-19 pandemic. , 111, 8853–8880. DOI: 10.1007/s11071-023-08282-4
- 37. Luo C., Liu L., Wang D. (2021). Multiscale financial risk contagion between international stock markets: Evidence from EMD—Copula—CoVaR analysis. , 58, 101512. DOI: 10.1016/j.najef.2021.101512
- 38. Nelsen R.B. (2006). . 2 ed. N.Y.: Springer-Verlag. 272 p. DOI: 10.1007/0-387-28678-0
- 39. Rodriguez J.C. (2007). Measuring financial contagion: A copula approach. , 14 (3), 401–423. DOI: 10.1016/j.jempfin.2006.07.002
- 40. Tian M., Guo F., Niu R. (2022). Risk spillover analysis of China's financial sectors based on a new GARCH copula quantile regression model. , 63, 101817. DOI: 10.1016/j.najef.2022.101817
- 41. Wang H., Yuan Y., Li Y., Wang X. (2021). Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory. , 94, 401–414. DOI: 10.1016/j.econmod.2020.10.002
- 42. Wen X., Wei Y., Huang D. (2012). Measuring contagion between energy market and stock market during financial crisis: A copula approach. , 34 (5), 1435–1446. DOI: 10.1016/j.eneco.2012.06.021
- 43. Zhang P., Lv Z.-X., Pei Z., Zhao Y. (2023). Systemic risk spillover of financial institutions in China: A copula-DCC-GARCH approach. , 11 (2), 100078. DOI: 10.1016/j.jer.2023.100078