Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of the US Banking Sector

27 Pages Posted: 6 Nov 2018

See all articles by Gianluca Cubadda

Gianluca Cubadda

University of Rome Tor Vergata - Department of Economics and Finance

Alain Hecq

Maastricht University - Department of Quantitative Economics

Antonio Riccardo

ICE Data Services Italy

Date Written: October 29, 2018

Abstract

This paper compares the forecasting performances of both univariate and multivariate models for realized volatilities series. We consider realized volatility measures of the returns of 13 major banks traded in the NYSE. Since our variables are characterized by the presence of long range dependence, we use several modelling approaches that are able to capture such feature. We look at the forecasting accuracy of the considered models to make inference on the underlying mechanism that has generated volatilities of the assets. Our main conclusion is that the contagion effect among the considered volatilities is small or, at least, not well captured by the considered multivariate models.

Keywords: Common volatility, long-memory processes, HAR models, index models, forecasting.

JEL Classification: C32

Suggested Citation

Cubadda, Gianluca and Hecq, Alain and Riccardo, Antonio, Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of the US Banking Sector (October 29, 2018). CEIS Working Paper No. 445. Available at SSRN: https://ssrn.com/abstract=3274826 or http://dx.doi.org/10.2139/ssrn.3274826

Gianluca Cubadda (Contact Author)

University of Rome Tor Vergata - Department of Economics and Finance ( email )

Via Columbia n.2
Roma, 00133
Italy

Alain Hecq

Maastricht University - Department of Quantitative Economics ( email )

P.O. Box 616
Maastricht, 6200 MD
Netherlands

HOME PAGE: http://www.maastrichtuniversity.nl/a.hecq

Antonio Riccardo

ICE Data Services Italy

Via Cristoforo Colombo, 149
Rome, 00147
Italy

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