Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.