IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/022381.html

Shocks propagation mechanism analysis on Russian commodity exchanges: The example of The Moscow Exchange

Author

Listed:
  • Tina Rakic

    (Saint Petersburg State University, Saint Petersburg, Russian Federation)

  • Lyudmila Gadasina

    (Saint Petersburg State University, Saint Petersburg, Russian Federation)

Abstract

Events over the past twenty years have demonstrated that the largest crises have been provoked by external shocks, with financial and commodity markets playing a significant role in their spread. This paper aims to identify and describe the shock propagation mechanism using the example of the Moscow Exchange commodity market. This study explores the time series volatility of commodity futures contracts for the period from January 2021 to February 2025. This work implements a new approach to time series analysis — Connectedness Approach based on the TVP-VAR model. This study contributes to the expansion of research on the analysis of shock propagation mechanisms in Russian financial markets in two ways: by applying a methodology for assessing connectivity and by analyzing data from the commodity market on the Moscow Exchange. The paper shows that oil is a source of changes in the commodity market. It is shown that oil is a source of changes that are passed on to precious metals. During the period under review, gas remained relatively independent of other commodities. The results obtained can be used by investors when forming a securities portfolio, businesses to develop financial management strategies, or exchange institutions in making regulatory decisions.

Suggested Citation

  • Tina Rakic & Lyudmila Gadasina, 2026. "Shocks propagation mechanism analysis on Russian commodity exchanges: The example of The Moscow Exchange," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 81, pages 46-67.
    • Handle: RePEc:ris:apltrx:022381
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Mishra, Aswini Kumar & Ghate, Kshitish, 2022. "Dynamic connectedness in non-ferrous commodity markets: Evidence from India using TVP-VAR and DCC-GARCH approaches," Resources Policy, Elsevier, vol. 76(C).
    2. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    3. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    4. de Jong, Johan & Sonnemans, Joep & Tuinstra, Jan, 2022. "The effect of futures markets on the stability of commodity prices," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 176-211.
    5. Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel & Gabauer, David & Dwumfour, Richard Adjei, 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, Elsevier, vol. 51(C).
    6. Naeem, Muhammad Abubakr & Chatziantoniou, Ioannis & Gabauer, David & Karim, Sitara, 2024. "Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach," International Review of Financial Analysis, Elsevier, vol. 91(C).
    7. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2021. "Interest rate swaps and the transmission mechanism of monetary policy: A quantile connectedness approach," Economics Letters, Elsevier, vol. 204(C).
    8. Andrew C. Meek & Seth A. Hoelscher, 2023. "Day-of-the-week effect: Petroleum and petroleum products," Cogent Economics & Finance, Taylor & Francis Journals, vol. 11(1), pages 2213876-221, December.
    9. Balli, Faruk & Balli, Hatice Ozer & Dang, Tam Hoang Nhat & Gabauer, David, 2023. "Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market," Finance Research Letters, Elsevier, vol. 57(C).
    10. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    11. Castro, Tomás del Barrio & Osborn, Denise R. & Taylor, A.M. Robert, 2012. "On Augmented Hegy Tests For Seasonal Unit Roots," Econometric Theory, Cambridge University Press, vol. 28(5), pages 1121-1143, October.
    12. Gabauer, David, 2021. "Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    13. Zhang, Dayong & Broadstock, David C., 2020. "Global financial crisis and rising connectedness in the international commodity markets," International Review of Financial Analysis, Elsevier, vol. 68(C).
    14. Camelia Minoiu & Chanhyun Kang & V.S. Subrahmanian & Anamaria Berea, 2015. "Does financial connectedness predict crises?," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 607-624, April.
    15. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    16. David Gabauer, 2020. "Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 788-796, August.
    17. Thomas A. Lubik & Christian Matthes, 2015. "Time-Varying Parameter Vector Autoregressions: Specification, Estimation, and an Application," Economic Quarterly, Federal Reserve Bank of Richmond, issue 4Q, pages 323-352.
    18. Marina Yu. Malkina, 2024. "Financial Contagion of the Commodity Markets from the Stock Market during Pandemic and New Sanctions Shocks," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(2), pages 452-475.
    19. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    20. Marina Yu. Malkina & Dmitry Yu. Rogachev, 2024. "Financial Contagion of the Russian Stock Market from the European Stock Market During the COVID-19 Pandemic," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 27-42, April.
    21. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    22. Wang, Gang-Jin & Chen, Yan & Zhu, You & Xie, Chi, 2024. "Systemic risk prediction using machine learning: Does network connectedness help prediction?," International Review of Financial Analysis, Elsevier, vol. 93(C).
    23. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    24. Marina Yu. Malkina & Anton O. Ovcharov, 2022. "Financial Contagion of Russian Companies from the Oil Market under the Influence of Sanctions and Pandemic Shock," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 8-28, August.
    25. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    26. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    27. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    28. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    29. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    30. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.
    31. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    32. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2021. "The day-of-the-week-effect on the volatility of commodities," Resources Policy, Elsevier, vol. 71(C).
    33. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    2. Huai, Jingliang & Cheung, Adrian (Wai Kong) & Wang, Bin, 2026. "On completing the connectedness analysis—A bootstrap-based DCC-GARCH approach," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).
    3. Wiesen, Thomas F.P. & Adekoya, Oluwasegun Babatunde & Oliyide, Johnson & Afatsao, Richard, 2024. "Does high volatility increase connectedness? A study of Asian equity markets," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    4. Stenfors, Alexis & Chatziantoniou, Ioannis & Gabauer, David, 2022. "Independent policy, dependent outcomes: A game of cross-country dominoes across European yield curves," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    5. Cocca, Teodoro & Gabauer, David & Pomberger, Stefan, 2024. "Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures," Energy Economics, Elsevier, vol. 136(C).
    6. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    7. Abakah, Emmanuel Joel Aikins & Brahim, Mariem & Carlotti, Jean-Etienne & Tiwari, Aviral Kumar & Mensi, Walid, 2024. "Extreme downside risk connectedness and portfolio hedging among the G10 currencies," International Economics, Elsevier, vol. 178(C).
    8. Papież, Monika & Rubaszek, Michał & Szafranek, Karol & Śmiech, Sławomir, 2022. "Are European natural gas markets connected? A time-varying spillovers analysis," Resources Policy, Elsevier, vol. 79(C).
    9. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.
    10. Xu, Danyang & Hu, Yang & Oxley, Les & Lin, Boqiang & He, Yongda, 2025. "Exploring the connectedness between major volatility indexes and worldwide sustainable investments," International Review of Financial Analysis, Elsevier, vol. 97(C).
    11. Naeem, Muhammad Abubakr & Chatziantoniou, Ioannis & Gabauer, David & Karim, Sitara, 2024. "Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach," International Review of Financial Analysis, Elsevier, vol. 91(C).
    12. Zhou, Bin & Shi, Huai-Long, 2024. "Quantile volatility connectedness among themes and sectors: Novel evidence from China," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    13. Su, Xianfang & Zhao, Yachao, 2025. "Asymmetric time-frequency risk spillovers between the Fourth Industrial Revolution assets and commodity futures: Is economic policy uncertainty a driving factor?," Global Finance Journal, Elsevier, vol. 64(C).
    14. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    15. Li, Hailing & Pei, Xiaoyun & Yang, Yimin & Zhang, Hua, 2024. "Assessing the impact of energy-related uncertainty on G20 stock market returns: A decomposed contemporaneous and lagged R2 connectedness approach," Energy Economics, Elsevier, vol. 132(C).
    16. Al-Nassar, Nassar S. & Assaf, Rima & Chaibi, Anis & Makram, Beljid, 2024. "The nexus between mineral, renewable commodities, and regional stock sectors during health and military crises," Resources Policy, Elsevier, vol. 96(C).
    17. Bhattacherjee, Purba & Mishra, Sibanjan & Kang, Sang Hoon, 2024. "Extreme time-frequency connectedness across U.S. sector stock and commodity futures markets," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1176-1197.
    18. Chen, Huayi & Shi, Huai-Long & Zhou, Wei-Xing, 2024. "Carbon volatility connectedness and the role of external uncertainties: Evidence from China," Journal of Commodity Markets, Elsevier, vol. 33(C).
    19. Su, Xianfang & Zhao, Yachao, 2025. "Risk spillovers between Chinese new energy futures and carbon-intensive assets: Asymmetric effect, time–frequency dynamics, and portfolio strategies," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    20. Le, Thanh Ha, 2023. "Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts," Renewable Energy, Elsevier, vol. 202(C), pages 613-625.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:022381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.