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

Anatoly Peresetsky and Ruslan I. Yakubov

International Journal of Computational Economics and Econometrics, 2017, vol. 7, issue 1/2, 152-169

Abstract: 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.

Keywords: financial market integration; stock markets; state space model; Kalman filter; non-synchronous data; market returns forecasting; autocorrelation; global stochastic trends; Japan; UK; United Kingdom; USA; United States. (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Working Paper: Autocorrelation in an unobservable global trend: Does it help to forecast market returns? (2015)
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