Commodity tail risks

In this study, we investigate the cross-section of option-implied tail risks in commodity markets. In contrast to findings from equity markets, left and right tail risks implied by option markets are both large. Commodity-specific variables exert the largest influence on tail risk, while there is no evidence of systematic commodity factors that are linked to tail risk. Additionally, we find strong links to the equity markets, but also comovements to macroeconomic factors. Left or right tail risks are largely independent of variance risk premiums. Finally, both left and right tail risks are priced in the cross-section of commodity futures returns

Option return predictability with machine learning and big data

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions, costly arbitrage, and option mispricing