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Autocorrelation in the global stochastic trend

Author

Listed:
  • Ruslan Durdyev

    (International Laboratory of Quantitative Finance NRU HSE)

  • Anatoly Peresetsky

    (Higher School of Economics and CEMI RAS, Moscow)

Abstract

Korhonen and Peresetsky (2013) suggested a new Kalman-filter type model of financial markets to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets. We extend this model to allow the correlation between increments of this global trend on neighbor intervals. Existence of that non-zero correlation is demonstrated. However it does not mean that it helps forecast daily returns of the stock indices itself, since the global stochastic trend is unobservable. Forecasting performance of the model with three stock markets is explored.

Suggested Citation

  • Ruslan Durdyev & Anatoly Peresetsky, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 39-58.
    • Handle: RePEc:ris:apltrx:0243
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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. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    3. 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.
    4. Lin, Wen-Ling & Engle, Robert F & Ito, Takatoshi, 1994. "Do Bulls and Bears Move across Borders? International Transmission of Stock Returns and Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 7(3), pages 507-538.
    5. Chang, Yoosoon & Isaac Miller, J. & Park, Joon Y., 2009. "Extracting a common stochastic trend: Theory with some applications," Journal of Econometrics, Elsevier, vol. 150(2), pages 231-247, June.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
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    Citations

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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. Григорьев Р.А., 2019. "Одновременные Эффекты Несинхронных Временных Рядов: Проблемы Var-Модели," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(2), pages 118-129, апрель.
    3. Polina Pogorelova & Anatoly Peresetsky, 2020. "Extracting the global stochastic trend from non-synchronous data on the volatility of financial indices," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 53-71.
    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.

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    Keywords

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    JEL classification:

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

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