Climate Change Uncertainty and Volatility of Clean Energy Portfolios: An Asset Pricing Perspective.

Using an asset pricing approach to model volatility, the authors investigate the effectiveness of recently developed textual analytics–based climate-related uncertainty indexes in improving forecasting of the volatility of clean energy portfolios. They assert that institutions investing in clean energy portfolios, as with any asset class, consider in addition to the risk–return characteristics, the uncertainty related to the impact of climate change risk on equity valuation and especially on the volatility of assets. They find that including a climate-related uncertainty index in a machine learning model with standard predictors from the finance literature improves the out-of-sample forecasting of volatility of clean energy portfolios. Their results for the impact of climate change uncertainty on clean energy portfolios are stronger than those documented in the energy economics literature for the relationship between climate change and clean energy ETF returns. [ABSTRACT FROM AUTHOR]