Abstract:
We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV)
model to examine the out-of-sample forecasting value of climate-risk factors for the realized volatility
of movements of the prices of crude oil, heating oil, and natural gas. The climate-risk factors have
been constructed in recent literature using techniques of computational linguistics, and consist of
daily proxies of physical (natural disasters and global warming) and transition (U.S. climate policy
and international summits) risks involving the climate. We find that climate-risk factors contribute
to out-of-sample forecasting performance mainly at a monthly and, in some cases, also at a weekly
forecast horizon. We demonstrate that our main finding is robust to various modifications of our
forecasting experiment, and to using three different popular shrinkage estimators to estimate the
extended HAR-RV model. We also study longer forecast horizons of up to three months, and we
account for the possibility that policymakers and forecasters may have an asymmetric loss function.
