Extracting global stochastic trend from non-synchronous data

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.

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Year of publication:	2013-06-19
Authors:	Korhonen, Iikka ; Peresetsky, Anatoly
Institutions:	Siirtymätalouksien tutkimuslaitos, Suomen Pankki
Subject:	emerging stock markets \| transition economies \| financial market integration \| stock market returns \| global stochastic trend \| state space model \| Kalman filter \| non-synchronous data

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Extent:	application/pdf
Series:	BOFIT Discussion Papers.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series BOFIT Discussion Papers Number 15/2013 22 pages
Classification:	C49 - Econometric and Statistical Methods: Special Topics. Other ; c58 ; F01 - Global Outlook ; F36 - Financial Aspects of Economic Integration ; G10 - General Financial Markets. General ; G15 - International Financial Markets
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010674216