Elicitability and Encompassing for Volatility Forecasts by Bregman Functions

In this paper, we construct a class of strictly consistent scoring functions based on the Bregman divergence measure, which jointly elicit the mean and variance. We use the scoring functions to develop a novel out-of-sample forecast encompassing test in volatility predictive models. We show the encompassing test is asymptotically normal. Simulation results demonstrate the merits of the proposed Bregman scoring functions and the forecast encompassing test. The forecast encompassing test exhibits a proper size and good power in finite samples. In an empirical application, we investigate the predictive ability of macroeconomic and financial variables in forecasting the equity premium volatility.