Authors: Anatoly A. Peresetsky; Ruslan I. Yakubov
Addresses: International Laboratory of Quantitative, Finance of the Higher School of Economics, Moscow, Russian Federation ' International Laboratory of Quantitative, Finance of the Higher School of Economics, Moscow, Russian Federation
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.
International Journal of Computational Economics and Econometrics, 2017 Vol.7 No.1/2, pp.152 - 169
Received: 21 Mar 2015
Accepted: 09 Jul 2015
Published online: 01 Dec 2016 *