forecasting. We consider option-implied volatilities, skewness, kurtosis, and densities. More generally, we discuss how any … consider results on option-implied covariance, correlation and beta forecasting, as well as the use of option … forecasting object that is a twice differentiable function of the future realization of the underlying risky asset price can …

Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and … proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which … powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting …