Model risk is investigated from a commercial banking viewpoint. We firstly analyze model misspecification. Then, the focus shifts towards model sensitivity. Finally, interactions among various models are scrutinized. Our overarching goal is to derive a distribution of indicators for summarizing...

We put forward a framework for measuring systemic risk and attributing it to individual banks. Systemic risk is measured as the expected loss to depositors and investors when a low-probability systemic event occurs. The risk contributions are calculated based on derivatives of the systemic risk...

We put forward a Merton-type multi-factor portfolio model for assessing banks’ contributions to systemic risk. This model accounts for the major drivers of banks’ systemic relevance: size, default risk and correlation of banks’ assets as a proxy for interconnectedness. We measure systemic...

A macro-prudential policy maker can manage risks to financial stability only if currentand future risks can be reliably assessed. We propose a novel framework to assessfinancial system risk. Using a dynamic factor framework based on state-space methods, we model latent macro-financial and credit...

We propose a fast algorithm for computing the economic capital, Value at Risk and Greeks in the Gaussian factor model. The algorithm proposed here is much faster than brute force Monte Carlo simulations or Fourier transform based methods. While the algorithm of Hull-White is comparably fast, it...

We put forward a Merton-type multi-factor portfolio model for assessing banks' contributions to systemic risk. This model accounts for the major drivers of banks' systemic relevance: size, default risk and correlation of banks' assets as a proxy for interconnectedness. We measure systemic risk...