We provide an economic valuation of the riskiness of risk models by directly measuring the impact of model risks (specification and estimation risks) on VaR estimates. We find that integrating the model risk into the VaR computations implies a substantial minimum correction of the order of...

This paper deals with stress tests for credit risk and shows how exploiting the discretion when setting up and implementing a model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model...

This paper deals with stress tests for credit risk and shows how exploiting the discretion when setting up and implementing a model can drive the results of a quantitative stress test for default probabilities. For this purpose, we employ several variations of a CreditPortfolioView-style model...

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...

The Global Financial Crisis (GFC) changes the relative economic riskiness and risk-adjusted-performance of different asset markets. While the empirical distribution for stock return shifted to the right and became more concentrated around the mean after the GFC, the real estate market...

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 marketor credit risk of an institution depend on statistical models the application of awrong model can lead to a serious over- or...

The Fundamental Review of the Trading Book (FRTB) presents greater challenges than the current Basel 2.5 Framework in calculating and managing market risk. Financial institutions often struggle to comply with regulatory timelines due to the complexity, difficulty, and expense of FRTB...

Yes, they can! Machine learning models that exploit big data identify leverage determinants and predict leverage better than classical methods. By allowing for nonlinearities and complex interactions, machine learning boosts the out-of-sample R-squared from 36% to 56% over linear methods such as...

This paper revisits the performance of frequently used risk forecasting methods, such as the Value-at-Risk models. The aim is to analyze its performance, and mitigate its pitfalls by incorporating conditional variance estimates, as generated by a GARCH model. Notably, this paper tests several...