Volatility forecasting using global stochastic financial trends extracted from non-synchronous data
Juan-Pablo Ortega and
Econometrics and Statistics, 2018, vol. 5, issue C, 67-82
A method based on various linear and nonlinear state space models used to extract global stochastic financial trends (GST) out of non-synchronous financial data is introduced. These models are constructed in order to take advantage of the intraday arrival of closing information coming from different international markets so that volatility description and forecasting is improved. A set of three major asynchronous international stock market indices is considered in order to empirically show that this forecasting scheme is capable of significant performance gains when compared to standard parametric models like the dynamic conditional correlation (DCC) family.
Keywords: Multivariate volatility modeling and forecasting; Global stochastic trend; Extended Kalman filter; Dynamic conditional correlations (DCC); Non-synchronous data (search for similar items in EconPapers)
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Working Paper: Volatility forecasting using global stochastic financial trends extracted from non-synchronous data (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:5:y:2018:i:c:p:67-82
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