Backtesting VaR and ES Under the Magnifying Glass

Backtesting provides the means of determining the accuracy of risk forecasts and

the corresponding risk model. Given that the actual return generating process is un-

known, the evaluation methods rely on various assumptions in order to quantify the

models inefficiencies and proceed with the model evaluation. These method specific

assumptions, in conjunction with the regulatory policies can introduce distortions

in the evaluation process, which affect the reliability of the evaluation results. To

investigate such effects from a practitioner's perspective, this paper reviews the ma-

jor Value at Risk and Expected Shortfall forecast evaluation methods and evaluates

their performance under a common simulation and financial application framework.

Our findings suggest that focusing on specific individual hypothesis tests provide

a more reliable alternative than the corresponding conditional coverage ones. In

addition, selecting a two-year out-of-sample period provides a significantly better

power to relevance ratio than the more relevant but powerless regulatory one-year

specification.