Forecasting Value-at-Risk for Model Risk Analysis in Energy Markets

We consider the assessment of mis-specification risk when forecastingValue-at-Risk on a daily horizon. In particular, we focus on Energy Markets (elec-tricity, oil, gas), where the impact of model risk may be relevant. Within an AR-GARCH framework to capture known features of volatility, we consider nine com-peting distributions for the standardized innovations and we apply a recently pro-posed measure of model risk to quantify the amount of model uncertainty in theprocedure. Our approach is made more robust by discarding, on a daily basis, theworst performing models by using a set of weights built upon the Bayesian Informa-tion Criterion. The analysis covers the period 2001-2015, allowing for an in-depthassessment of the dynamics of model risk.