An empirical analysis of the dynamic relationship between clean and dirty energy markets

This research provides an empirical analysis of the dynamic relationship between clean and dirty energy markets. Specifically, we use Brent crude, West-Texas-Intermediate (WTI) crude, OPEC oil, Crude oil Oman and Crude Oil Dubai to denote dirty energy markets and use the S&P Global Clean Energy Index and WilderHill New Energy Global Innovation Index as a representative of the clean energy market. The time-frequency wavelet's multiple cross-correlation and cross-quantilogram correlation are used as estimation techniques to examine time-dependent wavelet cross-correlation and directional predictability, respectively. We use daily returns spanning from November 2013 to September 2020. Findings from the cross-quantilogram correlation (CQC) results suggest heterogeneous quantile dependence dynamics from clean energy markets to dirty energy markets. Additionally, findings from the cross-quantile correlation results reveal positive and negative directional predictability between clean and dirty energy markets in high, medium and low quantile ranges. Second, results from the time-frequency wavelets multiple cross-correlation approach suggest that clean and dirty energy markets are marginally integrated at the lowest frequencies, with dirty energy emerging as a predictive power of clean energy. In addition, we also find that the co-movements between the clean and dirty energy sources are volatile in the medium and long term, thus reducing the medium- and long-term diversification sphere. These findings are relevant for portfolio managers and clean energy producers.