Distributional Predictability and Extreme Spillovers of Clean Energy, Steam Coal and Technology: Evidence from a Quantile-Based Analysis

From a new quantile perspective, this paper employs nonparametric quantile causality and quantile connectedness approach to investigate distributional predictability and spillover effects among steam coal, clean energy, and high-tech systems under normal and tail conditions, respectively. We first identify the quantile causality, while there is a unidirectional causality between the quantile orders 0.1 and 0.4 from high-tech to clean energy, indicating that the stock price of technology companies have predictive power of the stock prices of clean energy company when the latter is relatively low. Next, in terms of the quantile connectedness, while the risk shocks to the system do not propagate strongly around the median, there are strong spillover effects in both tails, where high-tech and clean energy contribute most of the system’s spillovers, and high-tech is the main net shock transmitter to all other variables. We further find that the strength of spillovers may depend on events such as the Paris Climate Agreement of 2015 and the COVID-19 pandemic.