Does electricity consumption impacting financial development? Wavelet analysis

Abstract

The current study analyzes the time–frequency dependencies between financial development (FD) and electrical power consumption (EPC) in the Gulf Cooperation Council (GCC) Countries (Qatar, Saudi Arabia, Kuwait, the United Arab Emirates, Oman, and Bahrain) during the period 1980–2017. The practical examination is based on the wavelet squared coherence that allows assessing the co-movement between the selected variables of FD and EPC. The main objective of this study is to investigate the short- and long-term dependencies between FD for the GCC countries and EPC to provide helpful insights to policy makers, investors, and government to show them if FD is influenced by EPC in GCC countries. Most of previous related studies have employed co-integration, causality, and panel data techniques. However, this study will be the first study that uses the wavelet coherence analysis to examine the relationship between EPC and FD. The results show periodic changes in the model of the co-movements especially after 2008 for all countries at comparatively high frequencies. Besides, the results indicate strength of co-movement varies by country. Such as a high degree of co-movement between the electrical power consumption and financial development in United Arab Emirates, Kingdom of Saudi Arabia, and Qatar, while low co-movement with Kuwait and Bahrain. The result as well document a comprehensive change in electrical power consumption and financial development co-movement after 2008 at comparatively low frequencies especially in Kuwait, Oman, and Bahrain. The results of the current study provide prospective importance implications for policy makers in improving energy plans for GCC countries that redound to EPC depression policies whereas conserving financial development.

Dynamic connectedness between green energy and carbon risk during Russia-Ukraine conflict: new evidence from a wavelet analysis

“…In the literature, previous studies also employ wavelet analysis to illustrate the different relationships at different times and frequencies. For example, similar findings are found between energy consumption and economic growth (Magazzino et al 2021 ), between renewable energy consumption as well as technological innovation and CO 2 emissions (Adebayo et al 2022 ), or between electricity consumption and financial development (Matar 2020 ).…”