BOFIT Discussion Papers, Institute for Economies in Transition, Bank of Finland
Extracting global stochastic trend from non-synchronous data
() and Anatoly Peresetsky
Abstract: We use a Kalman filter type model of financial markets to
extract a global stochastic trend from the discrete non-synchronous data on
daily stock market index returns of different stock exchanges. The model is
tested for robustness. In addition, we derive “most important” hours of
world financial market and estimate the relative importance of local versus
global news for different stock markets. The model generates results that
are consistent with intuition.
Keywords: emerging stock markets; transition economies; financial market integration; stock market returns; global stochastic trend; state space model; Kalman filter; non-synchronous data; (follow links to similar papers)
JEL-Codes: C49; C58; F01; F36; G10; G15; (follow links to similar papers)
22 pages, June 19, 2013
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