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Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models

Tamvakis, M. ORCID: 0000-0002-5056-0159, Marchese, M., Kyriakou, I. ORCID: 0000-0001-9592-596X and Di Iorio, F. (2020). Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models. Energy Economics, 88, 104757.. doi: 10.1016/j.eneco.2020.104757

Abstract

The relationship between the prices of crude oil and its refined products is at the heart of the oil industry. Crude oil and refined products volatilities and correlations have been mod- elled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using fractionally integrated multivariate GARCH models from a fore- casting and a risk management perspective. Several models for the spot returns on three major oil-related markets are compared. In-sample results show significant evidence of long-memory decay and leverage effects in volatilities and of time-varying autocorrelations. The forecasting performance of the models is assessed 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 covariance matrix and associated risk magnitudes. The paper makes an innovative contribution to the analysis of the relationship between crude oil and its refined products providing refiners, physical oil traders, non-commercial oil traders and other energy markets agents with significant insights for hedging and risk management operations.

Publication Type: Article
Additional Information: © 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: multivariate GARCH, long memory, Superior Predictive Ability test, Model Confidence Set, Value-at-Risk
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HG Finance
Departments: Business School > Actuarial Science & Insurance
Business School > Finance
Date Deposited: 15 Apr 2020 13:50
URI: https://openaccess.city.ac.uk/id/eprint/24039
[img] Text - Accepted Version
This document is not freely accessible until 7 November 2021 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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