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

Peresetsky, Anatoly and Yakubov, Ruslan (2015): Autocorrelation in an unobservable global trend: Does it help to forecast market returns?

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: financial market integration; stock market returns; state space model; Kalman filter; non-synchronous data; market returns forecast
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C49 - Other; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; F - International Economics > F3 - International Finance > F36 - Financial Aspects of Economic Integration; F - International Economics > F6 - Economic Impacts of Globalization > F65 - Finance; G - Financial Economics > G1 - General Financial Markets > G10 - General; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

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