Digest Finance
 

Analysis of Causal Relationships Between Oil Price Movements and RTS Index

Vol. 24, Iss. 4, DECEMBER 2019

PDF  Article PDF Version

Received: 6 March 2017

Received in revised form: 20 March 2017

Accepted: 4 April 2017

Available online: 25 December 2019

Subject Heading: SECURITIES AND FINANCIAL MARKETS

JEL Classification: G17

Pages: 438–447

https://doi.org/10.24891/df.24.4.438

Bogucharskov A.V. Financial University under Government of Russian Federation, Moscow, Russian Federation
bogucharskov92@mail.ru

ORCID id: not available

Subject This article explores a nonlinear cointegration between crude oil Brent price and RTS index for the period from 1 February 2006 to 1 February 2017.
Objectives The objective of the study is to investigate the impact of international oil prices on the Russian stock market, as well as to identify possible structural breaks in time series data.
Methods I apply the Gregory-Hansen test for threshold cointegration to investigate possible shifts of the endogenous and non-linear relationship between RTS index and oil price. The study also uses the Toda-Yamamoto version of Granger causality test. It represents the modified Wald test for identification of causal relationship between price oil movements and RTS index, after which it is possible to view the route of influence between variables.
Results The tests revealed structural breaks in the movements of variables and showed an increasing dependence between changes in oil prices and the Russian stock market after the 2008–2009 financial crisis. The research results may be applied to simulate the factor relationship of the Russian stock market.
Conclusions The impact of changes in international oil prices and other significant factors should be taken into account when making investment decisions and developing the financial policy of the State.

Keywords: RTS Index, crude oil Brent, Gregory-Hansen test, Toda-Yamamoto test

References:

  1. Reboredo J.C. How do crude oil prices co-move? A Copula Approach. Energy Economics, 2011, no. 33(5), pp. 948–955. URL: Link
  2. Reboredo J.C. Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Economics, 2015, vol. 47, iss. 7, pp. 32–45. URL: Link
  3. Fedorova E.A., Pankratov K.A. [Influence of macroeconomic indicators on Russian stock market]. Problemy prognozirovaniya = Problems of Forecast, 2010, no. 2, pp. 78–83. (In Russ.)
  4. Reboredo J.C., Ugolini A. Quantile dependence of oil price movements and stock returns. Energy Economics, 2016, vol. 54, pp. 33–49. URL: Link
  5. Qiang Ji. System analysis approach for the identification of factors driving crude oil prices. Computers and Industrial Engineering, 2012, vol. 63(3). URL: Link
  6. De Villiers J.U. Global Financial Markets. CFA Digest, 2001, vol. 31(3), pp. 46–47. URL: Link
  7. Gregory A.W., Hansen B.E. Residual-Based Tests For Cointegration in Models with Regime Shifts. Journal of Econometrics, 1992, vol. 70(1), pp. 99–126. URL: Link41685-7
  8. Ghosh S., Kanjilal K. Co-movement of International Crude Oil Price and Indian Stock Market: Evidences from nonlinear Cointegration tests. Energy Economics, 2016, vol. 53, no. 1, pp. 111–117. URL: Link
  9. Ivanova M.A. [Analysis of causal relationship between inflation and wages in Russia]. Problemy prognozirovaniya = Problems of Forecasting, 2016, no. 5, pp. 119–132. (In Russ.)
  10. Phillips P.C.B. Time Series Regression With a Unit Root. Econometrica, 1987, vol. 55, no. 2, pp. 277–301. URL: Link
  11. Kilian L., Park C. The impact of oil price shocks on the U.S. stock market. International Economic Review, 2009, vol. 50, iss. 4, pp. 1267–1287. URL: Link
  12. Reboredo J.C. Nonlinear effects of oil shocks on stock returns: A Markov-switching approach. Applied Economics, 2010, vol. 42, iss. 29, pp. 3735–3744. URL: Link
  13. Toda H.Y., Yamamoto T. Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. Journal of Econometrics, 1995, vol. 66, no. 1-2, pp. 225–250. URL: Link01616-8
  14. Clarke J.A., Mirza S. A comparison of some common methods for detecting Granger noncausality. Journal of Statistical Computation and Simulation, 2006, vol. 76, no. 3, pp. 207–231. URL: Link
  15. Ghosh S. Oil price shocks on Indian economy: Evidence from Toda Yamamoto and Markov regime-switching VAR. Macroeconomics and Finance in Emerging Market Economies, 2014, no. 7(1), pp. 122–139. URL: Link
  16. Apergis N., Miller S.M. Do Structural Oil-Market Shocks Affect Stock Prices? URL: Link
  17. Miller J.I., Ratti R.A. Crude oil and stock markets: Stability, instability, and bubbles. Energy Economics, 2009, no. 31(4), pp. 559–568. URL: Link

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