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

International Journal of Computational Economics and Econometrics. 2017. Vol. 7. No. 1-2. P. 152–169.

Peresetsky A., Yakubov R.

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: финансовые рынки, Япония, США, Japan, financial market integration, интеграция финансовых рынков, UK, автокорреляция, Kalman filter, фильтр Калмана, global stochastic trend, state space model, глобальный стохастический тренд, несинхронные данные, Соединенное Королевство, non-synchronous data, United States, US, прогноз доходностей финансовых рынков, stock markets, market returns forecasting, autocorrelation, модель пространства состояний