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

Malvina Marchese, Ioannis Kyriakou, Michael Tamvakis and Francesca Di Iorio

Energy Economics, 2020, vol. 88, issue C

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 modelled extensively using short-memory multivariate GARCH models. This paper investigates the potential benefits from using fractionally integrated multivariate GARCH models from a forecasting 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.

Keywords: Multivariate GARCH; Long memory; Superior predictive ability test; Model confidence set; Value-at-risk (search for similar items in EconPapers)
JEL-codes: C32 C51 C52 Q40 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:88:y:2020:i:c:s0140988320300967

DOI: 10.1016/j.eneco.2020.104757

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