We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture...

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...

These notes aim at giving a broad skill set to the actuarial profession in insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding....