Due to their short lifespans and migrating moneyness, options are notoriously difficult to study with the factor models commonly used to analyze the risk-return trade-off in other asset classes. Instrumented principal components analysis solves this problem by tracking contracts in terms of their pricing-relevant characteristics via time-varying latent factor loadings. We find that a model with three latent factors prices the cross-section of option returns and explains more than 85% of the variation in a panel of monthly S&P 500 option returns from 1996 to 2017. In particular, we show that the IPCA factors can be rationalized via an economically plausible three-factor model consisting of a level, slope and skew factor. Finally, out-of-sample trading strategies based on insights from the IPCA model have significant alpha over previously studied option strategies.

We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.