I demonstrate that much of the time series variation in the credit spread on high yield bonds is attributable to changes in the “credit risk premium” rather than changes in expected default losses. The credit risk premium is the expected excess return investors earn from bearing default risk...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

This paper offers a joint estimation approach for forecasting probabilities of default and loss rates given default in the presence of selection. The approach accommodates fixed and random risk factors. An empirical analysis identifies bond ratings, borrower characteristics and macroeconomic...

We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments...

As supply chain channels physical, financial, and information flows as well as associated risks, a firm’s supply chain information should be helpful in understanding and predicting its credit risks. Credit ratings as an approximate but important measure of corporate credit risks have been...

In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks...