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...

Automated valuation models have been in use at least for the last fifty years in both academia and practice, while a proper definition was coined only in the last decade. This could be mostly backed by the fact that research done on the automated valuation models is mostly empirical and...

The recent experience from the global financial crisis has raised serious doubts about the accuracy of standard risk measures as a tool to quantify extreme downward risks. Standard risk measures are subject to a “model risk” due to the specification and estimation uncertainty. We propose a...

The purpose of this paper is to investigate whether a dynamic Value at Risk model and high frequency realized volatility models can improve the accuracy of 1-day ahead VaR forecasting beyond the performance of frequently used models. As such, this paper constructs 60 conditional volatility...

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...

Performance evaluation of mutual funds using factor pricing models is usually distorted by the existence of a volatility anomaly and correlated residuals. By augmenting the Fama-French five-factor model with an active peer benchmark, we eliminate the measurement errors caused by these...

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 study investigates the link between capital market discipline and bank-level credit risk with a special emphasis on the role of bank ownership structure. Focusing on a large emerging market, Turkey, characterized by prominent state bank presence, our baseline regression results indicate...