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Autocorrelation in an unobservable global trend: does it help to forecast market returns?

Listed author(s):
  • Anatoly A. Peresetsky
  • Ruslan I. Yakubov

In this paper, a Kalman filter-type model is used to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets. The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market returns, since the global trend is unobservable. The performance of the model for the forecast of market returns is explored for three markets: Japan, UK, USA.

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Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Computational Economics and Econometrics.

Volume (Year): 7 (2017)
Issue (Month): 1/2 ()
Pages: 152-169

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Handle: RePEc:ids:ijcome:v:7:y:2017:i:1/2:p:152-169
Contact details of provider: Web page: http://www.inderscience.com/browse/index.php?journalID==311

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