In this article, we link the realized accuracy of predictive panels to changes in distributions that occur between the training (in-sample) phase and the testing (out-of-sample) phase. We obtain polynomial upper bounds for the loss of accuracy between training and testing. We model covariate...

The aim of this paper is to obtain the risk-neutral density of an underlying asset price as a function of its option implied volatility smile. We derive a known closed form non-parametric expression for the density and decompose it into a sum of lognormal and adjustment terms. By analyzing this...

In this article, we investigate the impact of truncating training data when fitting regression trees. We argue that training times can be curtailed by reducing the training sample without any loss in out-of-sample accuracy as long as the prediction model has been trained on the tails of the...