IDEAS home Printed from https://ideas.repec.org/a/beo/journl/v65y2020i227p67-94.html

The Effects Of Volatility And Changes In Conditional Correlations In The Stock Markets Of Russia And Developed Countries

Author

Listed:
  • Oleg N. Salmanov
  • Natalia V. Babina
  • Marina V. Samoshkina
  • Irina P. Drachena
  • Irina P. Salmanova

Abstract

The aim of this article is to identify patterns of profitability volatil-ity and to establish the degree of dynamic conditional correlation between the stock markets of developed countries and those of Russia. This issue is important for in-vestment strategies and the international diversification of investments. We use the BEKK-GARCH, CCC-GARCH, and DCC-GARCH models and show that the corre-lation between the Russian stock market and the markets of the USA, UK, Germany, and France has decreased significantly in recent years. We find that while the corre-lation between the Russian market and the mature European markets is bidirectional, the relationship between the US market and the Russian market is unidirectional. An assessment of the transfer of volatil-ity from all of the mature markets to the Russian market establishes its statistical significance and shows that feedback from the Russian market to the UK and German markets is insignificant. Diversification of international portfolios in the Russian market is recommended.

Suggested Citation

  • Oleg N. Salmanov & Natalia V. Babina & Marina V. Samoshkina & Irina P. Drachena & Irina P. Salmanova, 2020. "The Effects Of Volatility And Changes In Conditional Correlations In The Stock Markets Of Russia And Developed Countries," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 65(227), pages 67-94, October –.
    • Handle: RePEc:beo:journl:v:65:y:2020:i:227:p:67-94
    as

    Download full text from publisher

    File URL: http://www.ekof.bg.ac.rs/wp-content/uploads/2020/12/03.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Apostolos Serletis & Nahiyan Faisal Azad, 2020. "Emerging Market Volatility Spillovers," The American Economist, Sage Publications, vol. 65(1), pages 78-87, March.
    3. Ahmad, Wasim & Mishra, Anil V. & Daly, Kevin, 2018. "Heterogeneous dependence and dynamic hedging between sectors of BRIC and global markets," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 117-133.
    4. Sudharshan Reddy Paramati & Rakesh Gupta & Kishore Tandon, 2016. "Dynamic analysis of time-varying correlations and cointegration relationship between Australia and frontier equity markets," International Journal of Business and Emerging Markets, Inderscience Enterprises Ltd, vol. 8(2), pages 121-145.
    5. Kocaarslan, Baris & Sari, Ramazan & Gormus, Alper & Soytas, Ugur, 2017. "Dynamic correlations between BRIC and U.S. stock markets: The asymmetric impact of volatility expectations in oil, gold and financial markets," Journal of Commodity Markets, Elsevier, vol. 7(C), pages 41-56.
    6. Saleem, Kashif, 2008. "International linkage of the Russian market and the Russian financial crisis : a multivariate GARCH analysis," BOFIT Discussion Papers 8/2008, Bank of Finland, Institute for Economies in Transition.
    7. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    8. Anatolyev, Stanislav, 2008. "A 10-year retrospective on the determinants of Russian stock returns," Research in International Business and Finance, Elsevier, vol. 22(1), pages 56-67, January.
    9. Neha Seth & Monica Singhania, 2018. "Volatility in frontier markets: a Multivariate GARCH analysis," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 16(3), pages 294-312, December.
    10. Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2018. "Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 385-400, April.
    11. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    12. Salmanov, Oleg & Zaernjuk, Victor & Lopatina, Olga & Drachena, Irina & Vikulina, Evgeniya, 2016. "Investigating the Impact of Monetary Policy using the Vector Autoregression Method," MPRA Paper 112280, University Library of Munich, Germany, revised 01 Jun 2016.
    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. Chia-Lin Chang & Tai-Lin Hsieh & Michael McAleer, 2016. "Connecting VIX and Stock Index ETF," Tinbergen Institute Discussion Papers 16-010/III, Tinbergen Institute, revised 23 Jan 2017.
    2. Kearney, Colm & Muckley, Cal, 2008. "Can the traditional Asian US dollar peg exchange rate regime be extended to include the Japanese yen?," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 870-885, December.
    3. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    4. Miguel A. Ferreira, 2005. "Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework," Journal of Financial Econometrics, Oxford University Press, vol. 3(1), pages 126-168.
    5. Massimiliano Caporin & Michael McAleer, 2011. "Thresholds, news impact surfaces and dynamic asymmetric multivariate GARCH," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(2), pages 125-163, May.
    6. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
    7. Abdul Hakim & Michael McAleer, 2009. "VaR Forecasts and Dynamic Conditional Correlations for Spot and Futures Returns on Stocks and Bonds," CIRJE F-Series CIRJE-F-676, CIRJE, Faculty of Economics, University of Tokyo.
    8. Hakim, Abdul & McAleer, Michael, 2009. "Forecasting conditional correlations in stock, bond and foreign exchange markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2830-2846.
    9. Nicola, Francesca de & De Pace, Pierangelo & Hernandez, Manuel A., 2016. "Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment," Energy Economics, Elsevier, vol. 57(C), pages 28-41.
    10. Fong, P.W. & Li, W.K. & An, Hong-Zhi, 2006. "A simple multivariate ARCH model specified by random coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1779-1802, December.
    11. Akhtaruzzaman, Md & Shamsuddin, Abul & Easton, Steve, 2014. "Dynamic correlation analysis of spill-over effects of interest rate risk and return on Australian and US financial firms," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 378-396.
    12. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    13. David McMillan & Isabel Ruiz & Alan Speight, 2010. "Correlations and spillovers among three euro rates: evidence using realised variance," The European Journal of Finance, Taylor & Francis Journals, vol. 16(8), pages 753-767.
    14. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
    15. Kim, Bong-Han & Kim, Hyeongwoo & Min, Hong-Ghi, 2013. "Reassessing the link between the Japanese yen and emerging Asian currencies," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 306-326.
    16. Stefano Grassi & Francesco Violante, 2021. "Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas," Working Papers 2021-05, Center for Research in Economics and Statistics.
    17. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "The impact of Euro through time: Exchange rate dynamics under different regimes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1375-1408, January.
    18. Nakatani, Tomoaki & Teräsvirta, Timo, 2008. "Positivity constraints on the conditional variances in the family of conditional correlation GARCH models," Finance Research Letters, Elsevier, vol. 5(2), pages 88-95, June.
    19. Michael McAleer, 2009. "The Ten Commandments For Optimizing Value‐At‐Risk And Daily Capital Charges," Journal of Economic Surveys, Wiley Blackwell, vol. 23(5), pages 831-849, December.
    20. Marshall, Andrew & Maulana, Tubagus & Tang, Leilei, 2009. "The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 250-259, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:beo:journl:v:65:y:2020:i:227:p:67-94. 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: Marina Lečei (email available below). General contact details of provider: https://edirc.repec.org/data/efbeoyu.html .

    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.