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Extracting global stochastic trend from non-synchronous data

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  • Korhonen, Iikka
  • Peresetsky, Anatoly

Abstract

We use a Kalman filter type model of financial markets to extract a global stochastic trend from the discrete non-synchronous data on daily stock market index returns of different stock exchanges. The model is tested for robustness. In addition, we derive "most important" hours of world financial market and estimate the relative importance of local versus global news for different stock markets. The model generates results that are consistent with intuition. Key words: emerging stock markets, transition economies, financial market integration, stock market returns, global stochastic trend, state space model, Kalman filter, non-synchronous data. JEL codes: C49, C58, G10, G15, F36, F65

Suggested Citation

  • Korhonen, Iikka & Peresetsky, Anatoly, 2013. "Extracting global stochastic trend from non-synchronous data," BOFIT Discussion Papers 15/2013, Bank of Finland, Institute for Economies in Transition.
  • Handle: RePEc:bof:bofitp:2013_015
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    References listed on IDEAS

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    1. Korhonen, Iikka & Peresetsky, Anatoly, 2013. "What determines stock market behavior in Russia and other emerging countries?," BOFIT Discussion Papers 4/2013, Bank of Finland, Institute for Economies in Transition.
    2. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 85-118, Suppl. De.
    3. Byung Yoon Bae & Dong Heon Kim, 2011. "Global and Regional Yield Curve Dynamics and Interactions: The Case of Some Asian Countries," International Economic Journal, Taylor & Francis Journals, vol. 25(4), pages 717-738, December.
    4. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
    5. Cartea, Álvaro & Karyampas, Dimitrios, 2011. "Volatility and covariation of financial assets: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3319-3334.
    6. Mardi Dungey & Vance L Martin & Adrian R Pagan, 2000. "A multivariate latent factor decomposition of international bond yield spreads," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 697-715.
    7. Felices, Guillermo & Wieladek, Tomasz, 2012. "Are emerging market indicators of vulnerability to financial crises decoupling from global factors?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 321-331.
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    Cited by:

    1. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    2. repec:eee:ecosta:v:5:y:2018:i:c:p:67-82 is not listed on IDEAS
    3. Durdyev, Ruslan & Peresetsky, Anatoly, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 35(3), pages 39-58.
    4. Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2018. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," Econometrics and Statistics, Elsevier, vol. 5(C), pages 67-82.

    More about this item

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • F65 - International Economics - - Economic Impacts of Globalization - - - Finance

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