Measuring uncertainty and uncertainty dispersion from a large set of model predictions

Abstract

We construct measures of uncertainty and its dispersion exploiting the heterogeneity of a large set of model predictions. The approach is forward-looking, can be computed in real-time, and can be applied at any frequency. We illustrate the methodology with expected shortfall predictions of worldwide equity indices generated from 71 risk models. We use the new measures in asset pricing, risk forecasting, and for explaining the aggregate trading volume of S&P 500 firms.