This paper considers the properties of risk measures, primarily Value–at–Risk (VaR), from both internal and external (regulatory) points of view. It is argued that since market data is endogenous to market behavior, statistical analysis made in times of stability does not provide much guidance in times of crisis. In an extensive survey across data classes and risk models, the empirical properties of current risk forecasting models are found to be lacking in robustness while being excessively volatile. For regulatory use, the VaR measure is lacking in the ability to fulfill its intended task, it gives misleading information about risk, and in some cases may actually increase both idiosyncratic and systemic risk. Finally, it is hypothesized that risk modelling is not an appropriate foundation for regulatory design, and alternative mechanisms are discussed.
