Information spillovers and dynamic dependence between China’s energy and regional CET markets with portfolio implications: New evidence from multi-scale analysis

Abstract

Global climate change caused by human activities has posed a huge threat to the environmental governance and sustainability of human-being. Fortunately, the carbon emission trading scheme, a powerful tool for reducing carbon emissions, has been established in many countries to slow catastrophic climate change thus promoting human well-being. The nexuses between energy and European carbon markets have been extensively studied due to the grim reality of global climate change. However, no literature to date has examined the multi-scale associations between China’s energy and regional CET markets. This paper aims to fill this gap by utilizing a proposed wavelet-based multi-scale investigation framework to explore the information spillovers and dynamic dependence over different time horizons. The original energy and carbon return series are decomposed by the MODWT method. The TVP-VAR-based connectedness and the DECO-FIGARCH model are then built at each selected wavelet component. The wavelet coherence method is also applied to depict the time-frequency dependence. The empirical results demonstrate that the information spillovers at multivariate time-scales are heterogeneous. Generally, GDEA and the crude oil markets act as the information spillover net-transmitters at the raw data level and all wavelet scales. The dynamic spillovers present significant time-varying features and a similar evaluation trajectory. Besides, we offer strong evidence of a low dynamic equicorrelation between China’s energy and regional CET markets, suggesting the portfolio diversification benefits. Furthermore, the information spillover and dependence are proved to be more significant at longer time horizons. Finally, the energy-carbon portfolios offer diversification opportunities, and the risk reduction effectiveness varies with wavelet scales. Moreover, the optimal-weighted portfolio exhibits the best risk management performance. • Wavelet-based multi-scale spillover and dependence analysis framework is proposed. • MODWT is applied to decompose the original data into various wavelet components. • TVP-VAR-based connectedness is utilized to quantify the information spillovers. • Wavelet-based-DECO-FIGARCH model is employed to examine the dynamic dependence. • Wavelet coherence method is used to depict the time-frequency dependence structure.