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

Anatoly Peresetsky and Ruslan Yakubov

MPRA Paper from University Library of Munich, Germany

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, US.

Keywords: financial market integration; stock market returns; state space model; Kalman filter; non-synchronous data; market returns forecast (search for similar items in EconPapers)
JEL-codes: C49 C58 F36 F65 G10 G15 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-cfn, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Autocorrelation in an unobservable global trend: does it help to forecast market returns? (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:64579

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