• Rogneda Vasilyeva Graduate School of Economics and Management, Ural Federal University, Ekaterinburg
  • Valentin Voytenkov Graduate School of Economics and Management, Ural Federal University, Ekaterinburg
  • Alina Urazbaeva Graduate School of Economics and Management, Ural Federal University, Ekaterinburg




stock markets, Moscow exchange index, cointegration analysis, volatility, Impulse Response Functions


Currently, financial markets are growing rapidly, which increases the necessity to examine the financial sector. Considering the Russian Federation, the amount of private investors has doubled in Russia since the beginning of 2020 (Finam, 2020). It is important to realize how cash flows between the largest stock market indices. The main hypothesis of the research suggests that the U.S., Germany, and China markets result in significant changes in the Russian stock market. The research objective is to determine the degree of the Russian stock market dependence on the markets of developed and developing countries using methods of econometric analysis. Daily data on S&P500, DAX30, Hang Seng, and Moscow Exchange Index from January 1, 2015, to December 31, 2019, were taken. The research method chosen is a cointegration approach, including the construction of vector autoregression and vector error-correction models and the application of Impulse Response Functions. The results of the Granger causality test reveal no significant interconnection between the Dax30 and the Moscow Stock Exchange Index; the S&P500 affects the Moscow Exchange Index, whereas the Russian stock market affects the Chinese one. According to the cointegration analysis, there is a strong positive influence of the American stock market on the Russian stock market, which does not decrease during the researched period. The stock indices of China and Germany show a weak quantitative influence and mixed dynamics for a long time. The results of the research could be used as recommendations for making management decisions by private investors, hedge funds and managers of large companies.


Alwadi, S., Tahir Ismail, M., Khazaleh, A., Ariffin Abdul Karim, S. B., Al Wadi, S., Alkhahazaleh, M. H., & Ariffin Addul Karim, S. (2011). Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model. In Applied Mathematical Sciences (Vol. 5, Issue 7). https://www.researchgate.net/publication/265205660

Berument, H., & Ince, O. (2005). Effect of S&P500’s return on emerging markets: Turkish experience. Applied Financial Economics Letters, 1(1), 59–64. https://doi.org/10.1080/1744654052000314662

Demidova, O. A. (2020). Lecture in econometrics. Autocorrelation, no. 3. https://www.hse.ru/mirror/pubs/share/358351124.pdf

Fantazzini, & Dean. (2008). An Econometric Analysis of Financial Data in Risk Management. Applied Econometrics, 10(2), 91–137. https://ideas.repec.org/a/ris/apltrx/0006.html

Fedorova, E. A. (2013). Evaluation of the Impact of the U.S., Chinese and German Stock Markets on the Russian Stock Market. Economic Analysis: Theory and Practice, 47, 350.

Fedorova, E. A., & Nazarova, Y. N. (2010). Identification of factors affecting the volatility of the stock market, using cointegration approach. Economic Analysis: Theory and Practice, 3.

Finam. (2020). The number of private investors on the MosExchange exceeded 7.5 million in October. Review from Finam. https://www.finam.ru/analysis/newsitem/kolichestvo-chastnyx-investorov-na-mosbirzhe-v-oktyabre-prevysilo-7-5-mln-chelovek-20201105-174629/

Kanas, A. (1998). Linkages between the US and European equity markets: Further evidence from cointegration tests. Applied Financial Economics, 8(6), 607–614. https://doi.org/10.1080/096031098332646

Karpov, K. (2017). Rating of MICEX index drivers. Analytical note of BCS. BCS Express. https://bcs-express.ru/novosti-i-analitika/reiting-draiverov-indeksa-mmvb

Khromov, E. A. (2010). Fundamental Analysis of Shares. Finance and Credit, 28, 412.

Kularatne, C. (2002). An Examination of the Impact of Financial Deepening on Long-Run Economic Growth:An Application of a VECM Structure to a Middle-Income Country Context. The South African Journal of Economics, 70(4), 300–319. https://doi.org/10.1111/j.1813-6982.2002.tb01185.x

Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79, 960–972. https://doi.org/10.1016/j.future.2017.08.033

Linkova, M. V. (2016). Technical analysis: concept, essence and axioms. Territory of Science, 3.

Mukherjee, P., & Bose, S. (2008). Does the stock market in India move with Asia? A multivariate cointegration-vector autoregression approach. Emerging Markets Finance and Trade, 44(5), 5–22. https://doi.org/10.2753/REE1540-496X440501

Pesaran, H. M., Schuermann, T., & Smith, L. V. (2009). Forecasting economic and financial variables with global VARs. International Journal of Forecasting, 25, 642–675. https://doi.org/10.1016/j.ijforecast.2009.08.007

Samoylov, D. V. (2010). Factors Influencing the RTS Index during the Financial Crisis of 2008-2009 and before it. HSE Economic Journal, 2.

Sarwar, S., Tiwari, A. K., & Tingqiu, C. (2020). Analyzing volatility spillovers between oil market and Asian stock markets. Resources Policy, 66(June 2019), 101608. https://doi.org/10.1016/j.resourpol.2020.101608

Shrestha, M., & Chowdhury, K. (2005). ARDL Modelling Approach to Testing the Financial Liberalisation Hypothesis. Faculty of Business - Economics Working Papers. https://ro.uow.edu.au/commwkpapers/121

Taveeapiradeecharoen, P., Chamnongthai, K., & Aunsri, N. (2019). Bayesian Compressed Vector Autoregression for Financial Time-Series Analysis and Forecasting. IEEE Access, 7, 16777–16786. https://doi.org/10.1109/ACCESS.2019.2895022

Taylor, S. J. (2007). Modelling Financial Time Series (2nd Edition). World Scientific Publishing Company. https://books.google.ru/books?id=xVLICgAAQBAJ

Urazbaeva, A., Voytenkov, V., & Groznykh, R. (2020). The analysis of COVID-19 impact on the internet and telecommunications service sector through modelling the dependence of shares of Russian companies on the American stock market. R-Economy, 6(3), 162–170. https://doi.org/10.15826/recon.2020.6.3.014

Xiao, L., & Dhesi, G. (2010). Dynamic linkages between the European and US stock markets. Proceedings - 3rd International Conference on Business Intelligence and Financial Engineering, BIFE 2010, 403–407. https://doi.org/10.1109/BIFE.2010.99

Zeltser, M. (2020). A study of the relationship between the U.S. and Russian stock markets. Analytical note of BCS. BCS Express.




How to Cite

Vasilyeva, R. ., Voytenkov, V. ., & Urazbaeva, A. . (2021). DYNAMIC LINKAGES BETWEEN STOCK MARKETS: EVIDENCE FROM USA, GERMANY, CHINA AND RUSSIA. Proceedings of CBU in Economics and Business, 2, 95-101. https://doi.org/10.12955/peb.v2.260