Forecasting Energy Price Volatilities and Correlations: New Evidence From Fractionally Integrated Multivariate Garch Models

Energy Economics, Forthcoming

22 Pages Posted: 20 Mar 2020

See all articles by Malvina Marchese

Malvina Marchese

Cass Business School, City, University of london

Ioannis Kyriakou

Bayes Business School (formerly Cass), City, University of London

Michael Tamvakis

City University London - The Business School

Francesca Di Iorio

Istituto Nazionale di Statistica

Date Written: February 25, 2020

Abstract

Energy price volatilities and correlations have been modeled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using multivariate fractionally integrated GARCH models from a forecasting and a risk management perspective. Several multivariate GARCH models for the spot returns on three major energy markets are compared. Our in-sample results show significant evidence of long-memory decay in energy price returns volatilities, leverage effects and time-varying auto-correlations. The one-step ahead forecasting performance of the models is assessed using several robust matrix loss functions by means of three approaches: the Superior Predictive Ability test, the Model Confidence Set and the Value-at-Risk. The results indicate that the multivariate models incorporating long-memory outperform the short-memory benchmarks in forecasting the conditional co-variance matrix and associated risk magnitudes.

Keywords: Multivariate GARCH, Long Memory, Superior Predictive Ability Test, Model

JEL Classification: C32, C51, C52, Q40

Suggested Citation

Marchese, Malvina and Kyriakou, Ioannis and Tamvakis, Michael and Di Iorio, Francesca, Forecasting Energy Price Volatilities and Correlations: New Evidence From Fractionally Integrated Multivariate Garch Models (February 25, 2020). Energy Economics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3544242 or http://dx.doi.org/10.2139/ssrn.3544242

Malvina Marchese (Contact Author)

Cass Business School, City, University of london ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

Ioannis Kyriakou

Bayes Business School (formerly Cass), City, University of London ( email )

Faculty of Actuarial Science & Insurance
106 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 (0)20 7040 8738 (Phone)
+44 (0)20 7040 8881 (Fax)

HOME PAGE: http://www.bayes.city.ac.uk/experts/I.Kyriakou

Michael Tamvakis

City University London - The Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

Francesca Di Iorio

Istituto Nazionale di Statistica ( email )

ISTAT
Via C. Balbo 16
I-00184 Roma
Italy

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