We empirically analyze the pricing of political uncertainty, guided by a theoretical model of government policy choice. To isolate political uncertainty, we exploit its variation around national elections and global summits. We find that political uncertainty is priced in the equity option...

We empirically analyze the pricing of political uncertainty, guided by a theoretical model of government policy choice. After deriving the model's predictions for option prices, we test those predictions in an international sample of national elections and global summits. We find that political...

We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML). The predictive information we identify is essentially uncorrelated with most of the...

We empirically analyze the pricing of political uncertainty, guided by a theoretical model of government policy choice. To isolate political uncertainty, we exploit its variation around national elections and global summits. We find that political uncertainty is priced in the equity option...

We propose a method for constructing conditional option return distributions. In our model, uncertainty about the future option return has two sources: Changes in the position and shape of the implied volatility surface that shift option values (holding moneyness and maturity fixed), and changes...

We empirically analyze the pricing of political uncertainty, guided by a theoretical model of government policy choice. To isolate political uncertainty, we exploit its variation around national elections and global summits. We find that political uncertainty is priced in the equity option...