IDEAS home Printed from https://ideas.repec.org/p/zbw/bofitp/bdp2008_004.html
   My bibliography  Save this paper

Global and regional links between stock markets - the case of Russia and China

Author

Listed:
  • Kozluk, Tomasz

Abstract

In a broad sample of developed and emerging economies over the past ten years we apply the approximate factor model in a search for common global and regional driving-forces in stock market returns and volatility. We focus particularly on two emerging stock markets - Russia and China, because of their unique characteristics and performance in the past years. We find that while Russian markets, like the CEEC region, substantially increased their integration with global stock markets, both the Chinese A- and B-share markets continued to move largely independently from global movements and only slightly increased in comovement with regional forces. We provide evidence of a general increase in global comovement of stock markets over the past decade and a decline in the role of regional forces, which imply a decrease of the effectiveness of cross-country hedging strategies.

Suggested Citation

  • Kozluk, Tomasz, 2008. "Global and regional links between stock markets - the case of Russia and China," BOFIT Discussion Papers 4/2008, Bank of Finland Institute for Emerging Economies (BOFIT).
  • Handle: RePEc:zbw:bofitp:bdp2008_004
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/212617/1/bofit-dp2008-004.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Wang, Ping & Liu, Aying & Wang, Peijie, 2004. "Return and risk interactions in Chinese stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(4), pages 367-383, October.
    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. Juan José Echavarría & Andrés González, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 14-66, December.
    2. Bonhomme, Stphane & Robin, Jean-Marc, 2009. "Consistent noisy independent component analysis," Journal of Econometrics, Elsevier, vol. 149(1), pages 12-25, April.
    3. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    4. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    5. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," Borradores de Economia 5273, Banco de la Republica.
    6. Claus Brand & Daniel Buncic & Jarkko Turunen, 2010. "The Impact of ECB Monetary Policy Decisions and Communication on the Yield Curve," Journal of the European Economic Association, MIT Press, vol. 8(6), pages 1266-1298, December.
    7. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    8. Kinateder, Harald & Wagner, Niklas, 2017. "Quantitative easing and the pricing of EMU sovereign debt," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 1-12.
    9. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    10. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    11. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    12. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    13. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    14. Nektarios Aslanidis & Charlotte Christiansen & Neophytos Lambertides & Christos S. Savva, 2019. "Idiosyncratic volatility puzzle: influence of macro-finance factors," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 381-401, February.
    15. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
    16. Rachida Ouysse, 2013. "Forecasting using a large number of predictors: Bayesian model averaging versus principal components regression," Discussion Papers 2013-04, School of Economics, The University of New South Wales.
    17. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    18. Norman R. Swanson & Nii Ayi Armah, 2011. "Diffusion Index Models and Index Proxies: Recent Results and New Directions," Departmental Working Papers 201114, Rutgers University, Department of Economics.
    19. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised May 2022.
    20. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2019. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Papers 1910.13960, arXiv.org, revised Oct 2020.

    More about this item

    Keywords

    stock markets; financial integration; Russia; China; global and regional integration;
    All these keywords.

    JEL classification:

    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:zbw:bofitp:bdp2008_004. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/bofitfi.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.