Volatility forecasting using global stochastic financial trends extracted from non-synchronous data
نویسندگان
چکیده
This paper introduces a method based on various linear and nonlinear state space models that are used to extract global stochastic financial trends (GST) out of non-synchronous financial data. More specifically, these models are constructed to take advantage of the intraday arrival of closing information coming from different international markets to improve volatility description and forecasting. A set of three major asynchronous international stock market indices is used in order to empirically show that this forecasting scheme is capable of significant performance gains when compared to standard models like the dynamic conditional correlation (DCC) family.
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