Publikation:

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

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Datum

2018

Autor:innen

Ortega, Juan-Pablo
Peresetsky, Anatoly

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https://doi.org/10.1016/j.ecosta.2017.01.003
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Published

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Econometrics and Statistics. 2018, 5, pp. 67-82. eISSN 2452-3062. Available under: doi: 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.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
510 Mathematik

Schlagwörter

Multivariate volatility modeling and forecasting Global stochastic trend Extended Kalman filter Dynamic conditional correlations (DCC) Non-synchronous data

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ISO 690GRIGORYEVA, Lyudmila, Juan-Pablo ORTEGA, Anatoly PERESETSKY, 2018. Volatility forecasting using global stochastic financial trends extracted from non-synchronous data. In: Econometrics and Statistics. 2018, 5, pp. 67-82. eISSN 2452-3062. Available under: doi: 10.1016/j.ecosta.2017.01.003
BibTex
@article{Grigoryeva2018-01Volat-41245,
  year={2018},
  doi={10.1016/j.ecosta.2017.01.003},
  title={Volatility forecasting using global stochastic financial trends extracted from non-synchronous data},
  volume={5},
  journal={Econometrics and Statistics},
  pages={67--82},
  author={Grigoryeva, Lyudmila and Ortega, Juan-Pablo and Peresetsky, Anatoly}
}
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