Using nonparametric copulas to measure crude oil price co-movements

• Published in: Energy economics Vol. 82; pp. 211 - 223
• Main Authors: Ho, Anson T.Y., Huynh, Kim P., Jacho-Chávez, David T.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01-08-2019; Elsevier Science Ltd
• Subjects: Co-movement; Crude oil; Crude oil prices; Dependence; Economic models; Empirical analysis; Energy economics; Estimation; Goodness of fit; Markets; Nonparametric copula; Nonparametric statistics; Petroleum; Tail dependence; Tails; Crude oil prices; C22; F41; C46; G32; Tail dependence; Co-movement; C51; Nonparametric copula
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2018.05.022

Tail dependence of crude oil price returns between four major benchmark markets are analyzed through the lenses of nonparametric copula models. This paper illustrates that nonparametric copula is flexible to incorporate important empirical patterns of tail dependence and provides better goodness-of-fit to the data than the optimal parametric copula. Estimation results show that the level and the structure of tail dependence of crude oil returns vary significantly depending on data frequency and the time period covered.
•Tail dependence of crude oil prices are analyzed using copula models.•Nonparametric copula incorporates important empirical patterns of tail dependence.•Nonparametric copula shows less dependence than the best-fitting parametric ones.•Tail dependence varies depending on data frequency and the time period covered.