Energy markets – Who are the influencers?

Abstract

The energy markets have recently undergone important transformations (e.g. deregulation, technological progress, renewable energy deployment and changing energy consumer behaviour) and witnessed a variety of crisis periods, affecting the relationships among energy commodities and their interactions with clean energy indices. This has implications for price discovery, asset allocation and risk management, which requires in-depth analysis to uncover and identify which energy indices (or forms of energy) lead others or are the most influential, while accounting for asymmetry and non-linearity characteristics. To uncover the complex structure of the relationship across the returns of seven different energy commodities and two clean energy stock indices, we apply Granger causality and transfer entropy in both static and dynamic approaches. The results from the Granger causality analysis identify the influence of the other energy products on natural gas, whereas the transfer entropy analysis reveals the importance of WTI oil and the influence of clean energy indices. Diesel is the most influenced energy commodity. A rolling windows analysis confirms those findings and shows evidence of a time-variation that reflects the impacts of crisis periods, especially the pandemic, on the dynamics of relationships.

Dynamics and Determinants of Market Integration of Green, Clean, Dirty Energy Investments and Conventional Stock Indices

“…Our current study is related to a growing literature focusing the information transmission across clean and dirty energy investments (Ferrer et al, 2018;Ferreira et al, 2021), crude oil and stock market indices (Dawar et al, 2021;Geng et al, 2021), and the factors affecting each of these assets (Batten et al, 2019;Saeed et al, 2020a). However, our current study is different in its focus on market integration among the above-mentioned and the determinants factors under various quantiles as well as the use of the DECO model that processes a large number of time series without encountering the problem of dimensionality.…”

ICEEMDAN-Based Transfer Entropy between Global Commodity Classes and African Equities

“…By taking a distinct focus from the previous studies that only focused on connectedness between either African stocks [61,62] or commodities [34,48,63] or between commodities and African stock markets [29,46], our study provides fresh evidence about the multiscale information flow between global commodities and African equities markets. Our findings corroborate the existing works that emphasise the essence of dynamic estimators as opposed to static measures in examining the interdependencies between either commodity classes only (see, e.g., [36][37][38][39][40]) or between commodities and other traditional assets (see, e.g., [34,41,42]).…”

“…Methodically, the use of transfer entropies, which quantifies the mutual information shared between financial markets, has gained attention in recent finance literature (see, e.g., [1,9,15,16,33,[47][48][49][50]). e relevance of entropybased assessment of market efficiency is envisaged from the overflow of stressed market conditions in recent periods [50].…”

A Hybrid Deep Learning‐Based Network for Photovoltaic Power Forecasting

“…However, different atmospheric variables such as temperature, solar irradiance, humidity, and cloud properties cause significant uncertainty in integrating PVs to microgrid (MG) [3][4][5][6][7]. In contrast, an effective PV power forecasting model greatly improves solar power utilization [8][9][10]. erefore, efficient forecasting models in the utility grid will operate the power grid economically and transfer the required energy to the end-users [11,12].…”