Generate QR code
Open Access
Subscription Access
Stability of the Causal Relationship Between the Oil Price and the Russian Stock Index
https://doi.org/10.34023/2313-6383-2025-32-4-37-48
Abstract
The article is devoted to analyzing the stability of the causal relationship between changes in oil prices and the dynamics of the Russian stock market, whose primary indicator is the RTS Index. The main goal of the research is to test the hypothesis of the persistence of a stable influence of oil price shocks (driven by fluctuations in supply, demand, and supply expectations) on the RTS Index amid structural shifts in the Russian economy during the period from 1999 to 2019.
The novelty of the approach proposed by the author lies in applying the core tool for studying causality – the Structural Vector Autoregression (SVAR) model – to decompose oil price shocks into their constituent sources based on the reasons for their occurrence and to assess the RTS Index's response to them. Additionally, the Moving Block Bootstrap (MBB) method is used to test the significance of changes in the stock index's impulse responses. The research results indicate that, despite the detection of a structural break in the stock market variable equation, the differences in its impulse responses to oil price shocks before and after this break are statistically insignificant, which confirms the proposed hypothesis. Thus, it can be argued that a stable causal relationship exists between oil prices and the Russian stock index throughout the entire analyzed period, including global economic crises and domestic economic transformations in Russia.
The study contributes to understanding the long-term dynamics of the interconnections between the commodity and financial sectors of the Russian economy, highlighting the critical importance of the energy component for the stability of the national stock market.
Keywords
Supporting agencies: The publication was prepared within the framework of the Academic Fund Program at HSE Saint Petersburg (grant «Sanctions, Oil Prices, and Russian Stock Indices: An Empirical Assessment of Causality Using Machine Learning Methods»).
About the Author
O. I. Sviridov
National Research University Higher School of Economics (HSE University)
Russian Federation
Russian Federation
Oleg I. Sviridov – Second-Year Postgraduate Student, Research Intern, Department of Economics, St. Petersburg School of Economics and Management
16, Soyuza Pechatnikov Str., Saint Petersburg, 190121
References
1. Hamilton J.D. Oil and the Macroeconomy Since World War II. Journal of Political Economy. 1983;91(2):228– 248. Available from: https://doi.org/10.1086/261140.
2. Kling J.L. Oil Price Shocks and Stock Market Behavior. The Journal of Portfolio Management. 1985;12(1):34–39. Available from: https://doi.org/10.3905/jpm.1985.409034.
3. Jones C.M., Kaul G. Oil and the Stock Markets. The Journal of Finance. 1996;51(2):228–248. Available from: https://doi.org/10.1111/j.1540-6261.1996.tb02691.x.
4. Chen N.F., Roll R., Ross S.A. Economic Forces and the Stock Market. Journal of Business. 1986;59(3):383–402. Available from: https://doi.org/10.1086/296344.
5. Huang R.D., Masulis R.W., Stoll H.R. Energy Shocks and Financial Markets. Journal of Futures Markets. 1996;16(1):1–27. Available from: https://doi.org/10.1002/(SICI)1096-9934(199602)16:1<1::AID-FUT1>3.0.CO;2-Q.
6. Kilian L. Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for The US Economy? The Review of Economics and Statistics. 2008;90(2):216– 240. Available from: https://doi.org/10.1162/rest.90.2.216.
7. Kilian L., Park C. The Impact of Oil Price Shocks on the US Stock Market. International Economic Review. 2009;50(4):1267–1287. Available from: https://doi.org/10.1111/j.1468-2354.2009.00568.x.
8. Ahmadi M., Behmiri N.B., Manera M. How Is Volatility in Commodity Markets Linked to Oil Price Shocks? Energy Economics. 2016;59(8):11–23. Available from: https://doi.org/10.1016/j.eneco.2016.07.006.
9. Bastianin A., Conti F., Manera M. The Impacts of Oil Price Shocks on Stock Market Volatility: Evidence from the G7 Countries. Energy Policy. 2016;98:160–169. Available from: https://doi.org/10.1016/j.enpol.2016.08.020.
10. Kilian L., Zhou X. Oil Prices, Exchange Rates and Interest Rates. Journal of International Money and Finance. 2022;126:Article 102679. Available from: https://doi.org/10.1016/j.jimonfin.2022.102679.
11. Dagher L., Hasanov F.J. Oil Market Shocks and Financial Instability in Asian Countries. International Review of Economics & Finance. 2023;84:182–195. Available from: https://doi.org/10.1016/j.iref.2022.11.008.
12. Wang Y., Wu C., Yang L. Oil Price Shocks and Stock Market Activities: Evidence from Oil-Importing and Oil-Exporting Countries. Journal of Comparative Economics. 2013;41(4):1220–1239. Available from: https://doi.org/10.1016/j.jce.2012.12.004.
13. Aganin A.D., Peresetskiy A.A. Ruble Exchange Rate Volatility: Oil and Sanctions. Applied Econometrics. 2018;(4):5–21.
14. Aganin A.D. Russian Stock Index Volatility: Oil and Sanctions. Voprosy Ekonomiki. 2020;(2):86–100. Available from: https://doi.org/10.32609/0042-8736-2020-2-86-100. (In Russ.)
15. Sviridov O., Skorobogatov A. A Causal Link Analysis from Oil Price to the Russian Stock Market. Applied Econometrics. 2025;77:5–24. Available from: https://doi.org/10.22394/1993-7601-2025-77-5-24.
16. Doan T., Litterman R., Sims C. Forecasting and Conditional Projection Using Realistic Prior Distributions. Econometric Reviews. 1984;3(1):1–100. Available from: https://doi.org/10.1080/07474938408800053.
17. Litterman R.B. Forecasting with Bayesian Vector Autoregressions – Five Years of Experience. Journal of Business & Economic Statistics. 1986;4(1):25–38. Available from: https://doi.org/10.1080/07350015.1986.10509491.
18. Primiceri G.E. Time Varying Structural Vector Autoregressions and Monetary Policy. The Review of Economic Studies. 2005;72(3):821–852. Available from: https://doi.org/10.1111/j.1467-937X.2005.00353.x.
19. Omori Y. et al. Stochastic Volatility with Leverage: Fast and Efficient Likelihood Inference. Journal of Econometrics. 2007;140(2):425–449. Available from: https://doi.org/10.1016/j.jeconom.2006.07.008.
20. Cafiso G., Missale A., Rivolta G. The Credit Channel of the Sovereign Spread: A Bayesian SVAR Analysis. Economic Modelling. 2025;144:Article 106984. Available from: https://doi.org/10.1016/j.econmod.2024.106984.
21. Boufateh T., Saadaoui Z. The Time-Varying Responses of Financial Intermediation and Inflation to Oil Supply and Demand Shocks in the US: Evidence from Bayesian TVP-SVAR-SV Approach. Energy Economics. 2021;102:Article 105535. Available from: https://doi.org/10.1016/j.eneco.2021.105535.
22. Demeshev B.B., Malakhovskaya O.A. Macroeconomic Forecasting with a Litterman’s BVAR Model. HSE Economic Journal. 2016;20(4):691–710. (In Russ.)
23. Borzykh O.A. Bank Lending Channel in Russia: A TVP-FAVAR Approach. Applied Econometrics. 2016;(3): 96–117. (In Russ.)
24. Sohag K. et al. Policy Uncertainty, Oil Price, Stock Market and Precious Metal Markets Volatility Spillovers in the Russian Economy. Ekonomika Regiona [Economy of Regions]. 2022;18(2):383–397. Available from: https://doi.org/10.17059/ekon.reg.2022-2-6.
25. Zubarev A.V., Kirillova M.A. Estimation of Russia's GDP Losses Due to Sanctions Using the Global Vector Autoregression Model. Voprosy Statistiki. 2023;30(1):18–26. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2023-30-1-18-26.
26. Lütkepohl H. New Introduction to Multiple Time Series Analysis. Heidelberg: Springer Berlin; 2005. Available from: https://doi.org/10.1007/978-3-540-27752-1.
27. Sims C.A., Zha T. Were There Regime Switches in U.S. Monetary Policy? American Economic Review. 2006;96(1):54–81. Available from: https://doi.org/10.1257/000282806776157678.
28. Lanne M., Lütkepohl H., Maciejowska K. Structural Vector Autoregressions with Markov Switching. Journal of Economic Dynamics and Control. 2010;34(2):121–131. Available from: https://doi.org/10.1016/j.jedc.2009.08.002.
29. Skorobogatov A.S. Monetary Policy and Long-Run Economic Stability. Voprosy Ekonomiki. 2024;8:28–49. (In Russ.) Available from: https://doi.org/10.32609/0042-8736-2024-8-28-49.
30. Kilian L. Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review. 2009;99(3):1053–1069. Available from: https://doi.org/10.1257/aer.99.3.1053.
31. Sims C.A. Macroeconomics and Reality. Econometrica: Journal of the Econometric Society. 1980;48(1):1–48. Available from: https://doi.org/10.2307/1912017.
32. Hamilton J.D. Time Series Analysis. Princeton: Princeton University Press; 1994.
33. Efron B. Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics. 1979;7(1):569–593. Available from: https://doi.org/10.1214/aos/1176344552.
34. Künsch H.R. The Jackknife and the Bootstrap for General Stationary Observations. The Annals of Statistics. 1989;17(3):1217–1241. Available from: https://doi.org/10.1214/aos/1176347265.
35. Jentsch C., Lunsford K.G. Asymptotically Valid Bootstrap Inference for Proxy SVARs. Journal of Business & Economic Statistics. 2022;40(4):1876–1891. Available from: https://doi.org/10.1080/07350015.2021.1990770.
36. Chow G.C. Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica: Journal of the Econometric Society. 1960;28(3):591–605. Available from: https://doi.org/10.2307/1910133.
Review
For citations:
Sviridov O.I. Stability of the Causal Relationship Between the Oil Price and the Russian Stock Index. Voprosy statistiki. 2025;32(4):37-48. (In Russ.) https://doi.org/10.34023/2313-6383-2025-32-4-37-48
Views:
28
Email this article 