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Title: Stochastic volatility model with an exogenous control process of news flow
Authors: Balash, Vladimir
Keywords: New intensity;Volatility jumps
Issue Date: 2013
Publisher: Brunel University, School of Information Systems, Computing and Mathematics
Abstract: We consider different volatility models augmented with news analytics data to examine the impact of news intensity on stock volatility. We provides a description of the data used in the empirical analysis and defines the measures of news intensity. Results for the variance homogeneity tests for days with different news intensity are also given. We also show that abnormal returns occur more likely in days with high news intensity. We propose the different modifications of the SV model. We proposed a way to test the hypothesis of a short-term impact of news intensity on volatility. The results show that news analytics data improves the quality of prediction of volatility of the SV model. For almost all FTSE100 companies, the hypothesis of a short-term impact of news on stock volatility is accepted. Negative news increase short-term stock volatility more likely than positive news.
Description: This thesis was submitted for the degree of Master of Philosophy and was awarded by Brunel University
Appears in Collections:Dept of Mathematics Theses
Mathematical Sciences

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