Volatility Forecasting Using Global Stochastic Financial Trends Extracted from Non-Synchronous Data

36 Pages Posted: 14 May 2015 Last revised: 8 Aug 2017

See all articles by Lyudmila Grigoryeva

Lyudmila Grigoryeva

University of Konstanz

Juan-Pablo Ortega

Centre National de la Recherche Scientifique (CNRS); Nanyang Technological University

Anatoly Peresetsky

National Research University Higher School of Economics

Date Written: May 12, 2015

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.

JEL Classification: C13, C32, G17.

Suggested Citation

Grigoryeva, Lyudmila and Ortega, Juan-Pablo and Ortega, Juan-Pablo and Peresetsky, Anatoly, Volatility Forecasting Using Global Stochastic Financial Trends Extracted from Non-Synchronous Data (May 12, 2015). Available at SSRN: https://ssrn.com/abstract=2605504 or http://dx.doi.org/10.2139/ssrn.2605504

Lyudmila Grigoryeva

University of Konstanz ( email )

Fach D-144
Universitätsstraße 10
Konstanz, D-78457
Germany

Juan-Pablo Ortega (Contact Author)

Centre National de la Recherche Scientifique (CNRS) ( email )

16 route de Gray
Besançon, 25030
France

HOME PAGE: http://juan-pablo-ortega.com

Nanyang Technological University ( email )

21 Nanyang Link
Singapore, 637371
Singapore

HOME PAGE: http://https://juan-pablo-ortega.com

Anatoly Peresetsky

National Research University Higher School of Economics ( email )

17 Malaya Ordynka Street
20 Myasnitskaya Street
Moscow, 119017
Russia

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