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

Lyudmila Grigoryeva, Juan-Pablo Ortega and Anatoly Peresetsky

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces a method based on the use of various linear and nonlinear state space models that uses non-synchronous data to extract global stochastic financial trends (GST). These models are specifically constructed to take advantage of the intraday arrival of closing information coming from different international markets in order to improve the quality of volatility description and forecasting performances. 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 improvements when compared with those obtained with standard models like the dynamic conditional correlation (DCC) family.

Keywords: multivariate volatility modeling and forecasting; global stochastic trend; extended Kalman filter; CAPM; dynamic conditional correlations (DCC); non-synchronous data (search for similar items in EconPapers)
JEL-codes: C32 C5 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/64503/1/MPRA_paper_64503.pdf original version (application/pdf)

Related works:
Journal Article: Volatility forecasting using global stochastic financial trends extracted from non-synchronous data (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:64503

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().