LAUSR

Abstract This study employs wavelet coherency, phase differences and spillover analysis to examine the dynamic connectedness between oil prices and stock returns of clean energy and technology companies. Multivariate Generalised Auto-Regressive Conditional Heteroscedasticity models are used to examine the conditional correlations, hedging performance, and to make a portfolio strategy. The wavelet coherency analysis shows a weak degree of association between oil prices and clean energy stock returns and between oil prices and technology companies’ stock returns in time and frequency scales. The phase differences study shows that all series move cyclically, with technology stock returns leading oil prices and stock returns of clean energy companies. Furthermore, the volatility spillovers findings reveal that the overall connectedness of the system is 0.43%, while the degree of connectedness is greater at lower frequencies (1–4 days) than at higher frequencies (more than 4 days). The results also suggest that the volatility is transmitted from technology companies to oil and clean energy markets at all frequencies and over the whole period. Policy implications and hedging and portfolio options are discussed.