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Volatility forecasting using global stochastic financial trends extracted from non-synchronous data

Volatility forecasting using global stochastic financial trends extracted from non-synchronous data

Publikationstyp: Zeitschriftenartikel
Publikationsstatus: Published
Autor/innen: Grigoryeva, Lyudmila; Ortega, Juan-Pablo; Peresetsky, Anatoly
Erscheinungsjahr: 2018
Erschienen in: Econometrics and Statistics ; 5 (2018). - S. 67-82. - eISSN 2452-3062
DOI (zitierfähiger Link): https://dx.doi.org/10.1016/j.ecosta.2017.01.003
Zusammenfassung:
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.
Fachgebiet (DDC): 510 Mathematik
Schlagwörter: Multivariate volatility modeling and forecasting Global stochastic trend Extended Kalman filter Dynamic conditional correlations (DCC) Non-synchronous data
Universitätsbibliographie: Ja
Begutachtet: Ja

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GRIGORYEVA, Lyudmila, Juan-Pablo ORTEGA, Anatoly PERESETSKY, 2018. Volatility forecasting using global stochastic financial trends extracted from non-synchronous data. In: Econometrics and Statistics. 5, pp. 67-82. eISSN 2452-3062. Available under: doi: 10.1016/j.ecosta.2017.01.003

@article{Grigoryeva2018-01Volat-41245, title={Volatility forecasting using global stochastic financial trends extracted from non-synchronous data}, year={2018}, doi={10.1016/j.ecosta.2017.01.003}, volume={5}, journal={Econometrics and Statistics}, pages={67--82}, author={Grigoryeva, Lyudmila and Ortega, Juan-Pablo and Peresetsky, Anatoly} }

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