Insights into credit loss rates: a global database

Summary

Focus

Credit risk was a key factor in the Great Financial Crisis and numerous other crises. Banks' overall credit losses tend to increase suddenly during a crisis from the typically low levels seen during "normal" times. The Covid-19 pandemic underscored the need for accurate credit risk assessments of bank balance sheets. In this paper, we present alternative micro- and macroprudential concepts and metrics to establish actual credit loss rates as well as forward-looking market- and macro-implied credit loss rate estimates for most jurisdictions worldwide. We also provide a public dashboard featuring 10 downloadable economy-level credit loss rate metrics, which will be updated regularly.

Contribution

This project aims to help close the long-standing data gap issue on economy-level credit loss information by providing a valuable public resource for researchers, policymakers and practitioners. Building upon previous work by Daniel Hardy and Christian Schmieder, we combine time series of actual credit losses with forward-looking market- and macro-implied credit loss estimates. We provide various credit loss rate series for as many jurisdictions worldwide as possible, which will be updated as new information becomes available. The estimates are available in a dashboard, and users can easily download the data sets of credit loss metrics for the desired jurisdictions and time periods. Additionally, we provide a tool for users to run simplified scenario analyses based on projected GDP growth paths.

Findings

The paper presents various metrics of credit loss rates derived from multiple sources, each with its own unique purpose and usefulness. While granular information, such as sector-level statistics, would be ideal for precise loss estimation, such data remain scarce. The economy-specific time series estimated in the paper can be valuable for credit loss analyses and projections, but future work on calibrations may be necessary. Given the challenges associated with anticipating peaks in credit loss rates, one option presented in this paper is to use GDP-implied loss rate simulations, akin to those typically applied in stress tests.

Abstract

Credit risk has played a significant role in many financial crises, including the great financial crisis. The COVID-19 pandemic also highlighted bank credit losses to the private sector. However, there remains a significant gap in terms of reliable economy-level credit risk data for financial stability analysis, given that such information is not readily available to the public in any systematic manner. Building upon the work of Hardy and Schmieder (2020), we derive time series of actual as well as forward-looking market- and macro-implied credit loss rates for the majority of jurisdictions around the world. Our database, intended as a public good, is available through a user-friendly interactive dashboard, which allows downloads of credit loss rate time series for the desired jurisdiction(s). Users are also able to run simple scenario analyses based on their projected GDP paths. The data series will be updated on an ongoing basis as new information is published by the original sources.

JEL classification: G01, G21, G33, P52

Keywords: credit risk, credit loss rates, data gap, forward-looking, loss given default (LGD), macro-implied, probability of default (PD), stress test