Исследование механизма распространения шоков на российских товарных биржах (на примере Московской биржи)
DOI:
https://doi.org/10.22394/1993-7601-2026-81-46-67Ключевые слова:
механизм распространения, внешний шок, товарная биржа, Московская биржа, волатильность, фьючерсный контракт, методика оценки связности, TVP-VARАннотация
События последнего двадцатилетия показали, что самые масштабные кризисы были спровоцированы внешними шоками, в распространении которых значимую роль сыграли финансовый рынок и рынки сырьевых товаров. Данная работа стремится выявить и описать механизмы распространения шоков на примере товарного рынка Московской биржи. В рамках исследования рассматриваются временные ряды волатильности товарных фьючерсных контрактов за период с января 2021 г. по февраль 2025 г. и используется недавно появившаяся методика оценки связности, основанная на TVP-VAR модели. Данная работа расширяет анализ механизмов распространения шоков на российских финансовых рынках в двух аспектах: применение методики оценки связности и анализ данных товарного рынка Московской биржи. Показано, что нефть выступает источником изменений, которые передаются на драгоценные металлы, а газ в рассматриваемом периоде оставался относительно независимым от других товаров.Скачивания
Библиографические ссылки
Валько Д. В. (2018). Календарные аномалии на российском фондовом рынке: тенденции последнего десятилетия. Финансы и кредит, 24 (3), 550–562. DOI: 10.24891/fc.24.3.550.
Ватрушкин С. В. (2014). Оценка временных эффектов для индексов акций московской биржи. Управление корпоративными финансами, 3, 158–171.
Лякин А. Н. (2018). Три кризиса по одному сценарию. Вестник Санкт-Петербургского университета. Экономика, 34 (1), 4–25. DOI: 10.21638/11701/spbu05.2018.101.
Малкина М. Ю. (2023). Финансовое заражение отраслей российской экономики от нефтяных шоков в период пандемии. Terra Economicus, 21 (2), 6–22. DOI: 10.18522/2073‑6606‑2023‑21‑2‑6‑22.
Малкина М. Ю. (2024а). Заражение на рынках сырьевых товаров в период финансового стресса. Финансы: теория и практика, 28 (3), 194–205. DOI: 10.26794/2587‑5671‑2024‑28‑3‑194‑205.
Малкина М. Ю. (2024б). Финансовое заражение рынков биржевых товаров от фондового рынка в период пандемического и новых санкционных шоков. Journal of Applied Economic Research, 23 (2), 452–475. DOI: 10.15826/vestnik.2024.23.2.018.
Малкина М. Ю., Овчаров А. О. (2022). Финансовое заражение российских компаний от рынка нефти под воздействием санкционного и пандемического шока. Финансовый журнал, 14 (4), 8–28. DOI: 10.31107/2075‑1990‑2022‑4‑8‑28.
Малкина М. Ю., Рогачев Д. (2023). М.Финансовое заражение российских компаний в период пандемии COVID‑19. Корпоративные финансы, 17 (3), 55–71. DOI: 10.17323/j.jcfr.2073‑0438.17.3.2023.55‑71.
Малкина М. Ю., Рогачев Д. Ю. (2024). Финансовое заражение российского фондового рынка от европейского в период пандемии COVID‑19. Финансовый журнал, 16 (2), 27–42. DOI: 10.31107/2075‑1990‑2024‑2‑27‑42.
Овчаров А. О., Терехов А. М. (2024). Финансовое заражение российской экономики: межотраслевой аспект. Финансы: теория и практика, 28 (3), 183–193. DOI: 10.26794/2587‑5671‑2024‑28‑3‑183‑193.
Щепелева М. А. (2017). Финансовое заражение: трансграничное распространение системного риска. Мировая экономика и международные отношения, 61 (1), 17–28. DOI: 10.20542/0131‑2227‑2017‑61‑1‑17‑28.
Alizadeh S., Brandt M. W., Diebold F. X. (2002). Range-based estimation of stochastic volatility models. The Journal of Finance, 57 (3), 1047–1091. DOI: 10.1111/1540‑6261.00454.
Antonakakis N., Chatziantoniou I., Gabauer D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13 (4), 84. DOI: 10.3390/jrfm13040084.
Ari Y. (2022). The CARR-volatility connectedness between USD/TRY and foreign banks in Turkey: Evidence by TVP-VAR. Applied Econometrics, 67, 5–26. DOI: 10.22394/1993‑7601‑2022‑67‑5‑26.
Balli F., Balli H. O., Dang T. H. N., Gabauer D. (2023). Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market. Finance Research Letters, 57, 104168. DOI: 10.1016/j.frl.2023.104168.
Baruník J., Křehlík T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16 (2), 271–296. DOI: 10.1093/jjfinec/nby001.
Castro T. del B., Osborn D. R., Taylor A. M. R. (2012). On augmented HEGY tests for seasonal unit roots. Econometric Theory, 28 (5), 1121–1143. DOI: 10.1017/S0266466612000060.
Chatziantoniou I., Gabauer D., Stenfors A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Economics Letters, 204, 109891. DOI: 10.1016/j.econlet.2021.109891.
Cunado J., Gabauer D., Gupta R. (2024). Realized volatility spillovers between energy and metal markets: A time-varying connectedness approach. Financial Innovation, 10, 12. DOI: 10.1186/s40854‑023‑00554‑7.
De Jong J., Sonnemans J., Tuinstra J. (2022). The effect of futures markets on the stability of commodity prices. Journal of Economic Behavior & Organization, 198, 176–211. DOI: 0.1016/j.jebo.2022.03.025.
Demirer M., Diebold F. X., Liu L., Yilmaz K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33 (1), 1–15. DOI: 10.1002/jae.2585.
Diebold F. X., Yilmaz K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28 (1), 57–66. DOI: 10.1016/j.ijforecast.2011.02.006.
Diebold F. X., Liu L., Yilmaz K. (2017). Commodity connectedness. Working Paper 23685. National Bureau of Economic Research. DOI: 10.3386/w23685.
Diebold F. X., Yilmaz K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119 (534), 158–171. DOI: 10.1111/j.1468‑0297.2008.02208.x.
Diebold F. X., Yılmaz K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182 (1), 119–134. DOI: 10.1016/j.jeconom.2014.04.012.
Gabauer D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680. DOI: 10.1016/j.mulfin.2021.100680.
Gabauer D., Chatziantoniou I., Stenfors A. (2023). Model-free connectedness measures. Finance Research Letters, 54, 103804. DOI: 10.1016/j.frl.2023.103804.
Gabauer D. (2020). Volatility impulse response analysis for DCC-GARCH models: The role of volatility transmission mechanisms. Journal of Forecasting, 39 (5), 788–796. DOI: 10.1002/for.2648.
Garman M. B., Klass M. J. (1980). On the estimation of security price volatilities from historical data. Journal of Business, 53 (1), 67–78.
Koop G., Pesaran M. H., Potter S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74 (1), 119–147. DOI: 10.1016/0304‑4076(95)01753‑4.
Lee T. K., Cho J. H., Kwon D. S., Sohn S. Y. (2019). Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Systems with Applications, 117, 228–242. DOI: 110.1016/j.eswa.2018.09.005.
Lubik T. A., Matthes C. (2015). Time-varying parameter vector autoregressions: Specification, estimation, and an application. Economic Quarterly, 101 (4), 323–352. DOI: 10.21144/eq1010403.
Meek A. C., Hoelscher S. A. (2023). Day-of-the-week effect: Petroleum and petroleum products. Cogent Economics & Finance, 11 (1), 2213876. DOI: 10.1080/23322039.2023.2213876.
Minoiu C., Kang C., Subrahmanian V. S., Berea A. (2015). Does financial connectedness predict crises? Quantitative Finance, 15 (4), 607–624. DOI: 10.1080/14697688.2014.968358.
Mishra A. K., Ghate K. (2022). Dynamic connectedness in non-ferrous commodity markets: Evidence from India using TVP-VAR and DCC-GARCH approaches. Resources Policy, 76, 102572. DOI: 10.1016/j.resourpol.2022.102572.
Naeem M. A., Chatziantoniou I., Gabauer D., Karim S. (2024). Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach. International Review of Financial Analysis, 91, 102986. DOI: 10.1016/j.irfa.2023.102986.
Pesaran H. H., Shin Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58 (1), 17–29. DOI: 10.1016/S0165‑1765(97)00214‑0.
Primiceri G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72 (3), 821–852. DOI: 10.1111/j.1467‑937X.2005.00353.x.
Qadan M., Idilbi-Bayaa Y. (2021). The day-of-the-week-effect on the volatility of commodities. Resources Policy, 71, 101980. DOI: 10.1016/j.resourpol.2020.101980.
Samitas A., Kampouris E., Kenourgios D. (2020). Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71, 101507. DOI: 10.1016/j.irfa.2020.101507.
Sims C. A. (1980). Macroeconomics and reality. Econometrica, 48 (1), 1–48. DOI: 10.2307/1912017.
Tiwari A. K., Abakah E. J. A., Gabauer D., Dwumfour R. A. (2022). Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID‑19 pandemic: Implications for hedging and investments strategies. Global Finance Journal, 51, 100692. DOI: 10.1016/j.gfj.2021.100692.
Vansteenkiste I. (2011). What is driving oil futures prices? Fundamentals versus speculation. Working Paper 1371. European Central Bank.
Wang G. J., Chen Y., Zhu Y., Xie C. (2024). Systemic risk prediction using machine learning: Does network connectedness help prediction? International Review of Financial Analysis, 93, 103147. DOI: 10.1016/j.irfa.2024.103147.
Zhang D., Broadstock D. C. (2020). Global financial crisis and rising connectedness in the international commodity markets. International Review of Financial Analysis, 68, 101239. DOI: 10.1016/j.irfa.2018.08.003.
Val’ko D. V. (2018). Calendar anomalies in the Russian stock market: Trends of the recent decade.
Finance and Credit, 24 (3), 550–562 (in Russian). DOI: 10.24891/fc.24.3.550.
Vatrushkin S. V. (2014). Otsenka vremennykh effektov dlya indeksov aktsii moskovskoi birzhi. Upravlenie Korporativnymi Finansami, 3, 158–171 (in Russian).
Lyakin A. N. (2018). Three crises in the Russian economy and one chain of events. St Petersburg University
Journal of Economic Studies, 34 (1), 4–25 (in Russian). DOI: 10.21638/11701/spbu05.2018.101.
Malkina M. Yu. (2023). Financial contagion from oil shocks during the pandemic: A cross‑sector analysis.
Terra Economicus, 21(2), 6–22 (in Russian). DOI: 10.18522/2073‑6606 2023-21-2-6-2.
Malkina M. Yu. (2024a). Contagion in commodity markets under financial stress. Finance: Theory and Practice, 28 (3), 194–205 (in Russian). DOI: 10.26794/2587‑5671‑2024‑28‑3‑194‑205.
Malkina M. Yu. (2024b). Financial contagion of the commodity markets from the stock market during pandemic and new sanctions shocks. Journal of Applied Economic Research, 23 (2), 452–475 (in Russian). DOI: 10.15826/vestnik.2024.23.2.018.
Malkina M. Yu., Ovcharov A. O. (2022). Financial contagion of Russian companies from the oil market under the influence of sanctions and pandemic shock. Financial Journal, 14 (4), 8–28 (in Russian). DOI: 10.31107/2075‑1990‑2022‑4‑8‑28.
Malkina M., Rogachev D. (2023). Financial contagion of Russian companies during the COVID‑19 pandemic. Journal of Corporate Finance Research, 17 (3), 55–71 (in Russian). DOI: 10.17323/j.jcfr.2073‑0438.17.3.2023.55‑71.
Malkina M. Yu., Rogachev D. Y. (2024). Financial contagion of the Russian stock market from the European stock market during the COVID‑19 pandemic. Financial Journal, 16 (2), 27–42 (in Russian).
DOI: 10.31107/2075‑1990‑2024‑2‑27‑42.
Ovcharov А. О., Terekhov A. M. (2024). Financial contagion of the Russian economy: Intersectoral aspect. Finance: Theory and Practice, 28 (3), 183–193 (in Russian). DOI: 10.26794/2587‑5671‑2024‑28‑3‑183‑19310.
Shchepeleva M. A. (2017). Financial contagion: Global transmission of systemic risk. World Economy and International Relations, 61 (1), 17–28 (in Russian). DOI: 10.20542/0131‑2227‑2017‑61‑1‑17‑28.
Alizadeh S., Brandt M. W., Diebold F. X. (2002). Range-based estimation of stochastic volatility models. The Journal of Finance, 57 (3), 1047–1091. DOI: 10.1111/1540‑6261.00454.
Antonakakis N., Chatziantoniou I., Gabauer D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13 (4), 84. DOI: 10.3390/jrfm13040084.
Ari Y. (2022). The CARR-volatility connectedness between USD/TRY and foreign banks in Turkey: Evidence by TVP-VAR. Applied Econometrics, 67, 5–26. DOI: 10.22394/1993‑7601‑2022‑67‑5‑26.
Balli F., Balli H. O., Dang T. H. N., Gabauer D. (2023). Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market. Finance Research Letters, 57, 104168. DOI: 10.1016/j.frl.2023.104168.
Baruník J., Křehlík T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16 (2), 271–296. DOI: 10.1093/jjfinec/nby001.
Castro T. del B., Osborn D. R., Taylor A. M. R. (2012). On augmented HEGY tests for seasonal unit roots. Econometric Theory, 28 (5), 1121–1143. DOI: 10.1017/S0266466612000060.
Chatziantoniou I., Gabauer D., Stenfors A. (2021). Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach. Economics Letters, 204, 109891. DOI: 10.1016/j.econlet.2021.109891.
Cunado J., Gabauer D., Gupta R. (2024). Realized volatility spillovers between energy and metal markets: A time-varying connectedness approach. Financial Innovation, 10, 12. DOI: 10.1186/s40854‑023‑00554‑7.
De Jong J., Sonnemans J., Tuinstra J. (2022). The effect of futures markets on the stability of commodity prices. Journal of Economic Behavior & Organization, 198, 176–211. DOI: 0.1016/j.jebo.2022.03.025.
Demirer M., Diebold F. X., Liu L., Yilmaz K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33 (1), 1–15. DOI: 10.1002/jae.2585.
Diebold F. X., Yilmaz K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28 (1), 57–66. DOI: 10.1016/j.ijforecast.2011.02.006.
Diebold F. X., Liu L., Yilmaz K. (2017). Commodity connectedness. Working Paper 23685. National Bureau of Economic Research. DOI: 10.3386/w23685.
Diebold F. X., Yilmaz K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119 (534), 158–171. DOI: 10.1111/j.1468‑0297.2008.02208.x.
Diebold F. X., Yılmaz K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182 (1), 119–134. DOI: 10.1016/j.jeconom.2014.04.012.
Gabauer D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680. DOI: 10.1016/j.mulfin.2021.100680.
Gabauer D., Chatziantoniou I., Stenfors A. (2023). Model-free connectedness measures. Finance Research Letters, 54, 103804. DOI: 10.1016/j.frl.2023.103804.
Gabauer D. (2020). Volatility impulse response analysis for DCC-GARCH models: The role of volatility transmission mechanisms. Journal of Forecasting, 39 (5), 788–796. DOI: 10.1002/for.2648.
Garman M. B., Klass M. J. (1980). On the estimation of security price volatilities from historical data. Journal of Business, 53 (1), 67–78.
Koop G., Pesaran M. H., Potter S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74 (1), 119–147. DOI: 10.1016/0304‑4076(95)01753‑4.
Lee T. K., Cho J. H., Kwon D. S., Sohn S. Y. (2019). Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Systems with Applications, 117, 228–242. DOI: 110.1016/j.eswa.2018.09.005.
Lubik T. A., Matthes C. (2015). Time-varying parameter vector autoregressions: Specification, estimation, and an application. Economic Quarterly, 101 (4), 323–352. DOI: 10.21144/eq1010403.
Meek A. C., Hoelscher S. A. (2023). Day-of-the-week effect: Petroleum and petroleum products. Cogent Economics & Finance, 11 (1), 2213876. DOI: 10.1080/23322039.2023.2213876.
Minoiu C., Kang C., Subrahmanian V. S., Berea A. (2015). Does financial connectedness predict crises? Quantitative Finance, 15 (4), 607–624. DOI: 10.1080/14697688.2014.968358.
Mishra A. K., Ghate K. (2022). Dynamic connectedness in non-ferrous commodity markets: Evidence from India using TVP-VAR and DCC-GARCH approaches. Resources Policy, 76, 102572. DOI: 10.1016/j.resourpol.2022.102572.
Naeem M. A., Chatziantoniou I., Gabauer D., Karim S. (2024). Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach. International Review of Financial Analysis, 91, 102986. DOI: 10.1016/j.irfa.2023.102986.
Pesaran H. H., Shin Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58 (1), 17–29. DOI: 10.1016/S0165‑1765(97)00214‑0.
Primiceri G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72 (3), 821–852. DOI: 10.1111/j.1467‑937X.2005.00353.x.
Qadan M., Idilbi-Bayaa Y. (2021). The day-of-the-week-effect on the volatility of commodities. Resources Policy, 71, 101980. DOI: 10.1016/j.resourpol.2020.101980.
Samitas A., Kampouris E., Kenourgios D. (2020). Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71, 101507. DOI: 10.1016/j.irfa.2020.101507.
Sims C. A. (1980). Macroeconomics and reality. Econometrica, 48 (1), 1–48. DOI: 10.2307/1912017.
Tiwari A. K., Abakah E. J. A., Gabauer D., Dwumfour R. A. (2022). Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID‑19 pandemic: Implications for hedging and investments strategies. Global Finance Journal, 51, 100692. DOI: 10.1016/j.gfj.2021.100692.
Vansteenkiste I. (2011). What is driving oil futures prices? Fundamentals versus speculation. Working Paper 1371. European Central Bank.
Wang G. J., Chen Y., Zhu Y., Xie C. (2024). Systemic risk prediction using machine learning: Does network connectedness help prediction? International Review of Financial Analysis, 93, 103147. DOI: 10.1016/j.irfa.2024.103147.
Zhang D., Broadstock D. C. (2020). Global financial crisis and rising connectedness in the international commodity markets. International Review of Financial Analysis, 68, 101239. DOI: 10.1016/j.irfa.2018.08.003.
