IDEAS home Printed from https://ideas.repec.org/a/bic/journl/v11y2011i1p109-124.html

Influence of news from Moscow and New York on returns and risks of Baltic States’ stock markets

Author

Listed:
  • Kurt Brannas

    (Umeå University)

  • Albina Soultanaeva

    (Umeå University)

Abstract

The impact of news from the Moscow and New York stock exchanges on the daily returns and volatilities of Baltic stock market indices is studied. A nonlinear time series model that accounts for asymmetries in conditional mean and variance functions is used for the empirical work. News from New York has stronger effects on returns in Tallinn than news from Moscow. High-risk shocks in New York have a stronger impact on volatility in Tallinn, whereas volatility in Vilnius is more influenced by high-risk shocks from Moscow. Riga seems not to be affected by news arriving from abroad.

Suggested Citation

  • Kurt Brannas & Albina Soultanaeva, 2011. "Influence of news from Moscow and New York on returns and risks of Baltic States’ stock markets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 11(1), pages 109-124, July.
    • Handle: RePEc:bic:journl:v:11:y:2011:i:1:p:109-124
    as

    Download full text from publisher

    File URL: https://www.tandfonline.com/doi/epdf/10.1080/1406099X.2011.10840493
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. P. Hartmann & S. Straetmans & C. G. de Vries, 2004. "Asset Market Linkages in Crisis Periods," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 313-326, February.
    2. Rockinger, Michael & Urga, Giovanni, 2001. "A Time-Varying Parameter Model to Test for Predictability and Integration in the Stock Markets of Transition Economies," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 73-84, January.
    3. Y. Angela Liu & Ming-Shiun Pan, 1997. "Mean and Volatility Spillover Effects in the U.S. and Pacific–Basin Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 1(1), pages 47-62, March.
    4. Kurt Brannas & Niklas Nordman, 2003. "Conditional skewness modelling for stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 725-728.
    5. Kurt Brännäs & Henry Ohlsson, 1999. "Asymmetric Time Series and Temporal Aggregation," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 341-344, May.
    6. Koutmos, Gregory & Booth, G Geoffrey, 1995. "Asymmetric volatility transmission in international stock markets," Journal of International Money and Finance, Elsevier, vol. 14(6), pages 747-762, December.
    7. Kristensen Dennis & Rahbek Anders, 2009. "Asymptotics of the QMLE for Non-Linear ARCH Models," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-38, April.
    8. Jan G. De Gooijer & Kurt Brännäs, 2004. "Asymmetries in conditional mean and variance: modelling stock returns by asMA-asQGARCH," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 155-171.
    9. repec:bla:jfinan:v:44:y:1989:i:1:p:1-17 is not listed on IDEAS
    10. Enrique Sentana, 1995. "Quadratic ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(4), pages 639-661.
    11. Gregory Koutmos, 1999. "Asymmetric index stock returns: evidence from the G-7," Applied Economics Letters, Taylor & Francis Journals, vol. 6(12), pages 817-820.
    12. Ulf Nielsson, 2007. "Interdependence of Nordic and Baltic Stock Markets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(2), pages 9-28, January.
    13. Kurt Brannas & Niklas Nordman, 2003. "An alternative conditional asymmetry specification for stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 537-541.
    14. Koutmos, Gregory, 1998. "Asymmetries in the Conditional Mean and the Conditional Variance: Evidence From Nine Stock Markets," Journal of Economics and Business, Elsevier, vol. 50(3), pages 277-290, May.
    15. Eun, Cheol S. & Shim, Sangdal, 1989. "International Transmission of Stock Market Movements," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(2), pages 241-256, June.
    16. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    17. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 1998. "Information and volatility linkages in the stock, bond, and money markets," Journal of Financial Economics, Elsevier, vol. 49(1), pages 111-137, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Viorica Chirilă & Ciprian Chirilă, 2020. "Asymmetric Return and Volatility Transmission in Euro Zone and Baltic Countries Stock Markets," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 2-11, December.

    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. Brännäs, Kurt & Soultanaeva, Albina, 2006. "Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices," Umeå Economic Studies 696, Umeå University, Department of Economics.
    2. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    3. Changli He & Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Parameterizing Unconditional Skewness in Models for Financial Time Series," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 208-230, Spring.
    4. Jan G. De Gooijer & Kurt Brännäs, 2004. "Asymmetries in conditional mean and variance: modelling stock returns by asMA-asQGARCH," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 155-171.
    5. Brännäs, Kurt, 2003. "Temporal Aggregation of the Returns of a Stock Index Series," Umeå Economic Studies 614, Umeå University, Department of Economics.
    6. Gagnon, Louis & Karolyi, G. Andrew, 2006. "Price and Volatility Transmission across Borders," Working Paper Series 2006-5, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    7. Thomas C. Chiang & Cathy W.S. Chen & Mike K.P. So, 2007. "Asymmetric Return and Volatility Responses to Composite News from Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 11(3-4), pages 179-210, September.
    8. Alistair Mees & Berndt Pilgram, 2000. "Non-Linear Markov Modelling Using Canonical Variate Analysis: Forecasting Exchange Rate Volatility," Econometric Society World Congress 2000 Contributed Papers 1162, Econometric Society.
    9. Poshakwale, Sunil S. & Aquino, Katty Pérez, 2008. "The dynamics of volatility transmission and information flow between ADRs and their underlying stocks," Global Finance Journal, Elsevier, vol. 19(2), pages 187-201.
    10. Aityan, Sergey K. & Ivanov-Schitz, Alexey K. & Izotov, Sergey S., 2010. "Time-shift asymmetric correlation analysis of global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 590-605, December.
    11. Giulio Cifarelli & Giovanna Paladino, 2001. "Volatility spillovers and the role of leading financial centres," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 54(216), pages 37-71.
    12. Wang, Steven Shuye & Firth, Michael, 2004. "Do bears and bulls swim across oceans? Market information transmission between greater China and the rest of the world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(3), pages 235-254, July.
    13. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
    14. Brännäs Kurt & De Gooijer Jan G. & Lönnbark Carl & Soultanaeva Albina, 2012. "Simultaneity and Asymmetry of Returns and Volatilities: The Emerging Baltic States' Stock Exchanges," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-24, January.
    15. repec:rza:wpaper:202 is not listed on IDEAS
    16. Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022. "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, vol. 60(C).
    17. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Exploiting Spillovers to Forecast Crashes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(8), pages 936-955, December.
    18. K. Lebedeva, 2015. "An Empirical Analysis of the Russian Financial Markets’ Liquidity and Returns," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 3(3), pages 5-31.
    19. Chen, Cathy W.S. & Yang, Ming Jing & Gerlach, Richard & Jim Lo, H., 2006. "The asymmetric reactions of mean and volatility of stock returns to domestic and international information based on a four-regime double-threshold GARCH model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 401-418.
    20. Koulakiotis, Athanasios & Babalos, Vassilios & Papasyriopoulos, Nicholas, 2016. "Financial crisis, liquidity and dynamic linkages between large and small stocks: Evidence from the Athens Stock Exchange," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 46-62.
    21. Pan, Ming-Shiun & Liu, Y. Angela & Roth, Herbert J., 1999. "Common stochastic trends and volatility in Asian-Pacific equity markets," Global Finance Journal, Elsevier, vol. 10(2), pages 161-172.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:bic:journl:v:11:y:2011:i:1:p:109-124. 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: Anna Zasova (email available below). General contact details of provider: https://edirc.repec.org/data/biceplv.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.