We provide evidence on the relationship between aggregate uncertainty and the macroeconomy. Identifying uncertainty shocks using methods from the news shocks literature, the analysis finds that innovations in realized stock market volatility are robustly followed by contractions, while shocks to forward-looking uncertainty have no significant effect on the economy. Moreover, investors have historically paid large premia to hedge shocks to realized but not implied volatility. A model in which fundamental shocks are skewed left can match those facts. Aggregate volatility matters, but it is the realization of volatility, rather than uncertainty about the future, that has been associated with declines.

We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high‐dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.

We propose and implement a procedure to dynamically hedge climate change risk. We extract innovations from climate news series that we construct through textual analysis of newspapers. We then use a mimicking portfolio approach to build climate change hedge portfolios. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. We discuss multiple directions for future research on financial approaches to managing climate risk.