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 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...

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...

<B>Russian Abstract: Цель данной работы – выявление наиболее значимых факторов внешней и внутренней среды (к которой относятся финансовые показатели и качество...</b>

This paper (1) introduces a modified-Machlup (mM) assessment, as well as DQA and DQE concerns and a DQXY Thesis that tie the mM assessment to the Duhem-Quine (DQ) Thesis and theories of model validation (TMV), and then (2) applies the mM and DQA/DQE concepts to three archetypal models (i.e.,...

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...