Most option pricing models assume all parameters except volatility are fixed; yet they almost invariably change on re-calibration. This paper explains how to capture the model risk that arises when parameters that are assumed constant have calibrated values that change over time and how to use...

We develop a new approach to evaluate asset pricing models (APMs) based on Minimum Discrepancy (MD) projections that generalize the Hansen-Jagannathan (HJ, 1997) distance to account for an arbitrary number of moments of asset returns. The Minimum Discrepancy projections correct APMs to become...

Model risk has a huge impact on any risk measurement procedure and its quantification is therefore a crucial step. In this paper, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...