Extraction of a Stochastic Global Trend on the Daily Stock Market Indices

Student: Morozova Daria

Educational Programme: Applied Economics (Master)

Year of Graduation: 2017

This paper proposes approaches to the volatility modeling and extraction of the global stochastic trend from daily closing values of the stock market. A Kalman-filter type model is suggested, which splits the daily returns of each index into two independent components — local and global. The model takes into account the asynchrony of the stock indices and possible autocorrelation in the global trend. The return-volatility is estimated by the autoregressive conditional heteroskedastic model. The performance of the Kalman-filter type model for the estimation of local and global components of return-volatilty is explored for three markets: Japan, UK, US. Ideas are proposed for further research.

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