Time-varying correlations in oil, gas and CO2 prices: an application using BEKK, CCC, and DCC-MGARCH models

Julien Chevallier (1)

1 Respiratory Epidemiology and Public Health

Previous literature has identified oil and gas prices as being the main drivers of CO2 prices in a univariate GARCH econometric framework (Alberola et al. (2008), Oberndorfer (2009)). By contrast, we argue in this article that the interrelationships between energy and emissions markets shall be modeled in a vector autoregressive and multivariate GARCH framework, so as to reflect the dynamics of the correlations between the oil, gas and CO2 variables overtime. Using BEKK, CCC, and DCC-MGARCH models on daily data from April 2005 to December 2008, we highlight significant own-volatility, cross-volatility spillovers, and own persistent volatility effects for nearly all markets, indicating the presence of strong ARCH and GARCH effects. Besides, we provide strong empirical evidence of time-varying correlations in the range of [-0.3;0.3] between oil and gas, [-0.05;0.05] between oil and CO2, and [-0.2;0.2] between gas and CO2, that have not been considered by previous studies. These findings are of interest for traders and utilities in the energy sector, but also for a broader applied economics audience.

Mots clés: Social Sciences & Humanities

Domaines: Économie et finance quantitative [q-fin]

Julien Chevallier. Time-varying correlations in oil, gas and CO2 prices: an application using BEKK, CCC, and DCC-MGARCH models. Applied Economics, 2011, pp.1. ⟨10.1080/00036846.2011.589809⟩. ⟨hal-00716634⟩