Convex optimization solutions tend to be unstable, to the point of entirely offsetting the benefits of optimization. For example, in the context of financial applications, it is known that portfolios optimized in-sample often underperform the naïve (equal weights) allocation out-of-sample. This...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

This paper explores a linear hedge fund replication and alternative beta methodology that is robust to the presence of non linearities and the possibility of model mis-specification. In a fashion similar to Roncalli and Weisang (2009a), the problem is cast as a tracking problem in order to allow...

We present a method for computing a normalized measure of model risk. The method is based on the definition of a "distance" between models, measured by the conditional entropy of a perturbed model with respect to a reference model. The method is illustrated by numerical examples

Model risk as part of the operational risk is a serious problem for financial institutions. As the pricing of derivatives as well as the computation of the market or credit risk of an institution depend on statistical models the application of a wrong model can lead to a serious over- or...

I entertain a generalization of the standard Bolzmann-Gibbs-Shannon measure of entropy in multiplier preferences of model uncertainty. Using this measure, I derive a generalized exponential certainty equivalent, which nests the exponential certainty equivalent of the standard Hansen-Sargent...