We investigate the cross-sectional return predictability of delta-hedged equity options using 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...

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...

We study an equilibrium asset pricing model with several Lucas (1978) trees subject to persistent distress events, where the agent has incomplete information about the state of an underlying common factor and learns from the events occurring to each tree. Contrary to similar asset pricing models...

The central ingredient of empirical asset pricing tests is the (expected) risk premium. However, heterogeneity in expectations makes aggregation of beliefs a non-trivial task. This paper proposes a novel approach to estimate subjective bond risk premia based on the historical accuracy of...