This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best...

We study factor pricing and market integration across major asset classes. Factor models specializing in one asset class have limited pricing power for other asset classes. Thus, we reject perfect market integration. However, an optimal integrated factor model across asset classes can...

We comprehensively investigate the usefulness of tail risk measures proposed in the literature. We evaluate both the statistical and the economic validity of the measures. The option-implied measure of Bollerslev and Todorov (2011b) (BT11Q) performs the best overall. While some other tail risk...

We develop a framework to quantify the convenience yield risk (CYR) inherent to each commodity futures market. Implementing our approach, we document that our novel CYR measure is informative about future commodity returns. In panel regressions, the CYR predicts future returns with a positive...