This paper presents time-varying contagion indices of credit risk spillover and feedback between 64 financials and sovereigns in the euro area, where spillover is identified based on bilateral Granger causality regressions. Over-identification of contagion between financials' true credit risk...

In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We compare the statistical performance of a simple and traditionally used model like the Logistic Regression...

This study investigates the need for credit supervision as conducted by on-site banking supervisors. It builds on a real bank on-site credit examination to compare the performance of a hypothetical self-supervision approach, in which banks themselves assess their loan portfolios without external...

Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional...

We study the interplay between two channels of interconnectedness in the banking system. The first one is a direct interconnectedness, via a network of interbank loans, banks' loans to other corporate and retail clients, and securities holdings. The second channel is an indirect...

Corporate credit spreads are modelled through a Hidden Markov model (HMM) which is based on a discretised Ornstein-Uhlenbeck model. We forecast the credit spreads within this HMM and filter out state-related information hidden in the observed spreads. We build a long short-term memory recurrent...

We represent the market for syndicated loans as a dynamic financial network and find that financial institutions have developed strong cross-border linkages over the 2000–2019 period. The most-connected financial institutions hold large shares of leveraged and covenant-lite loans. Our analysis...

We present a data-driven proof of concept model capable of reproducing expected counterparty credit exposures from market and trade data. The model has its greatest advantages in quick single-contract exposure evaluations that could be used in front office xVA solutions. The data was generated...

In this paper, we create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end-user. We analyze the explainability of these models for...

Using state-of-the-art recurrent neural network architectures, this study attempts to predict credit default swap risk premia for BR[I]CS countries as accurately as possible. In the time series setting, these recurrent neural networks are ELMAN, NARX, GRU, and LSTM RNNs, considering local and...