Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/27218
Authors: 
Sibbertsen, Philipp
Stahl, Gerhard
Luedtke, Corinna
Year of Publication: 
2008
Series/Report no.: 
Discussion papers // School of Economics and Management of the Hanover Leibniz University 409
Abstract: 
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.
Subjects: 
Risk evaluation
model risk
robust estimation
stress tests
JEL: 
C50
G32
Document Type: 
Working Paper

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