Measuring model risk

Model risk as part of the operational risk is a serious problem for financial institutions. As the pricing of derivatives as well as the computation of the market or credit risk of an institution depend on statistical models the application of a wrong model can lead to a serious over- or underestimation of the institution's risk. Because the underlying data generating process is unknown in practice evaluating the model risk is a challenge. So far, definitions of model risk are either application-oriented including risk induced by the statistician rather than by the statistical model or research-oriented and too abstract to be used in practice. Especially, they are not data-driven. We introduce a data driven notion of model risk which includes the features of the research-oriented approach by extending it by a statistical model building procedure and therefore compromises between the two definitions at hand. We furthermore suggest the application of robust estimates to reduce the model risk and advocate the application of stress tests with respect to the valuation of the portfolio.