We contribute to the literature by analyzing forecast combination methods in the context of machine learning to predict equity returns. Whilst individual models lack robustness, forecast combinations display stability and are able to produce improved results with Sharpe ratios up to 3.16. We use...

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...

In this paper, we study the quality of commodity futures markets. We investigate the impact of two major changes: (1) The influx of index investors after 2004 (financialization) and (2) the introduction of side-by-side trading of open-outcry and electronic limit order markets around mid-2006...

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...

In this paper, we empirically investigate the degree of systemic risk in the U.S. banking sector versus other industry sectors. We characterize the systemic risk in each sector by the lower tail dependence of stock returns. Our study differs from extant literature in three respects. First, we...