Extracting Global Stochastic Trend from Non-Synchronous Data

22 Pages Posted: 6 Jul 2013

See all articles by Iikka Korhonen

Iikka Korhonen

Bank of Finland - Institute for Economies in Transition (BOFIT)

Anatoly Peresetsky

National Research University Higher School of Economics

Date Written: June 19, 2013

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

JEL Classification: C49, C58, G10, G15, F36, F65

Suggested Citation

Korhonen, Iikka and Peresetsky, Anatoly, Extracting Global Stochastic Trend from Non-Synchronous Data (June 19, 2013). BOFIT Discussion Paper No. 15/2013, Available at SSRN: https://ssrn.com/abstract=2289790 or http://dx.doi.org/10.2139/ssrn.2289790

Iikka Korhonen (Contact Author)

Bank of Finland - Institute for Economies in Transition (BOFIT) ( email )

P.O.Box 160
Helsinki 00101
Finland

Anatoly Peresetsky

National Research University Higher School of Economics ( email )

17 Malaya Ordynka Street
20 Myasnitskaya Street
Moscow, 119017
Russia

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