We present an analysis of the sensitivity of household mortgage probabilities of default (PDs) and loss given default (LGDs) on unemployment rates, house price growth, interest rates, and other drivers. A structural micro-macro simulation model is used to that end. It is anchored in the balance...

We re-assess the view that sovereigns with a history of default are charged only a small and/or short-lived premium on the interest rate warranted by observed fundamentals. Our reassessment uses a metric of such a “default premium” (DP) that is consistent with asymmetric information models...

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from...

This paper incorporates house price risk and mortgages into a standard incomplete market (SIM) model. The model is calibrated to match U.S. data and accounts for non-targeted features of the data such as the distribution of down payments, the life-cycle profile of home ownership, and the...

Banks' living wills involve both recovery and resolution. Since it may not always be clear when recovery plans or actions should be triggered, there is a role for an objective metric to trigger recovery. We outline how such a metric could be constructed meeting criteria of (i) adequate loss...

This paper suggests a novel approach to assess corporate sector solvency risk. The approach uses a Bottom-Up Default Analysis that projects probabilities of default of individual firms conditional on macroeconomic conditions and financial risk factors. This allows a direct macro-financial link...

This simulation-based paper investigates the impact of different methods of dynamic provisioning on bank soundness and shows that this increasingly popular macroprudential tool can smooth provisioning costs over the credit cycle and lower banks' probability of default. In addition, the paper...

This paper offers novel evidence on the impact of raising bank capital requirements in the context of an emerging market: Peru. Using quarterly bank-level data and exploiting the adoption of bank-specific capital buffers, we find that higher capital requirements have a short-lived, negative...

Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...

Recent advances in digital technology and big data have allowed FinTech (financial technology)lending to emerge as a potentially promising solution to reduce the cost of credit and increasefinancial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credithave...