Climate Change and Volatility Forecasting: Novel Insights from Sectoral Indices

Abstract

This study investigates the impact of climate policy uncertainty (CPU) on sectoral indices and clean energy exchange-traded funds (ETFs) by using a GARCH-MIDAS model. The stock indices include renewable energy (NEX), transportation, mining, industrial, real estate, green economy US, green economy Europe, green economy Asia index, and ETFs clean energy (PBW), global clean energy (PBD). The research also evaluates the forecasting power of uncertainty factors, including equity market volatility (EMV), economy policy uncertainty (EPU), trade policy uncertainty (TPU), fiscal policy uncertainty (FPU), global economy policy uncertainty (GEPU), and geopolitical risk (GPR), to predict the volatility. We obtained some notable results. First, the out-of-sample findings show that CPU index information is useful to predict the volatility of the NEX renewable energy, green economy US, transportation, energy index for the US, and clean energy (PBW), global clean energy (PBD) ETFs. Second, EMV, EPU, and GPR also contain valuable information for the real estate, industrial, energy, and NEX renewable energy index. Additionally, we find evidence during low and high volatility and upheaval of the COVID-19 pandemic. The $R_{\mathrm{oos}}^2$ square and model confidence set (MCS) tests verify each model's out-of-sample forecasting performance.