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Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices

Author

Listed:
  • Brännäs, Kurt

    (Department of Economics, Umeå University)

  • Soultanaeva, Albina

    (Department of Economics, Umeå University)

Abstract

The impact of news of the Moscow and New York stock market exchanges on the returns and volatilities of the Baltic state stock market indices is studied using daily return data for the period of 2000-2005. A nonlinear time series model that accounts for asymmetries in the conditional mean and variance functions is used for the em- pirical work. News from New York have stronger effect on returns in Tallinn, than news from Moscow. High risk shocks in New York have a strong impact on volatility in Tallinn, whereas volatility of Vilnius is more influenced by high risk shocks from Moscow. Riga seems to be autonomous to news arriving from abroad.

Suggested Citation

  • 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.
  • Handle: RePEc:hhs:umnees:0696
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    References listed on IDEAS

    as
    1. 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.
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    3. Kurt Brannas & Niklas Nordman, 2003. "Conditional skewness modelling for stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 725-728.
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    7. Brännäs, Kurt & Nordman, Niklas, 2001. "An Alternative Conditional Asymmetry Specification for Stock Returns," Umeå Economic Studies 556, Umeå University, Department of Economics.
    8. 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.
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    More about this item

    Keywords

    Estonia; Latvia; Lithuania; Time series; Estimation; Finance;
    All these 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

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