Autocorrelation in an unobservable global trend: does it help to forecast market returns?. (2017)

• Record Type: Journal Article
• Authors: Peresetsky, Anatoly A.; Yakubov, Ruslan I.
• Is Part Of: International journal of computational economics and econometrics. Volume 7:Number 1/2(2017)
• Page Start: 152
• Page End: 169
• Publication Date: 2017
• Subjects: 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; Econometrics -- Periodicals; Economics -- Data processing -- Periodicals; 330.01519505
• Languages: English
• ISSNs: 1757-1170

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.