Global Credit Data members work together to analyse the data and discuss methodology issues. GCD has published up until now the following papers.
Interested in a more condensed view? Explore our GCD Recovery Rate Dashboards and Interactive Dashboard. They provide an instant insight into observed recovery levels and other key benchmarks for various exposure classes, industry sectors and collateral types.
Unresolved Defaults LGD Study 2020
This report describes the Global Credit Data (GCD) methodology for calculating loss given defaults (LGDs) for unresolved loans. The methodology benefits from GCD’s detailed and granular collection of post-default cash flow data and is based on extrapolations of historical recovery cash flows refined by the usage of risk drivers.
The methodology provides a straightforward, data-driven way of incorporating incomplete workout processes in the estimation of longrun average LGDs. Extensive validation both in- and out-of-sample has shown that the method works well in predicting LGDs for unresolved defaults.
Downturn LGD Study 2020
This Global Credit Data (GCD) study looks into the historical effects of previous downturns on bank credit losses across various debtor types, industries and regions, with a view to helping banks understand not only the high-level impacts of a downturn, but also how credit risk drivers are impacted, including sector specific impacts across different portfolio types. Combined with banks’ independent inputs for key risk drivers – including macroeconomic forecasts, portfolio biases, and the differences between the current and previous crises – the data in this report equips banks with the fundamental tools necessary to make accurate adjustments to their credit loss estimates for the COVID-19 crisis.
LGD Report Large Corporates 2020
How can banks project losses in the current Covid-19 crisis? asks Global Credit Data in latest report on loss given default
The analyses in this study offer an overall insight into LGD data on a global level and confirm the drivers, their direction and their levels shown in the 2019 and 2018 reports.
• Seniority and collateral are again confirmed as LGD drivers. Secured LGD is lower than unsecured LGD, particularly where a strong (primary) collateral is held.
• After aggregating country-level data to regions, North America and Europe appear to have similar levels of LGD – 23% and 21% respectively.
PD Benchmarking Report 2019
The report, which is designed to help banks benchmark their Probability of Default (PD) estimates against industry peers, highlights the conservative nature of banks’ internal PD estimates, with average PD estimates over the last 15 years standing at 1.63% compared to an average default rate of 0.90% over the same period.
For the report, GCD collected information regarding long-term internal observed default rates and internal rating migration matrices from a portfolio of 26 leading financial institutions, over a period of 15 years. This data is highly valuable for benchmarking key risk processes within banks, such as PD rating scale calibration, PD model calibration, regulatory and economic capital calculation and stress testing, among others.
IFRS 9 Benchmarking Report 2019
As the first IFRS 9 statements are being released, banks, investors, auditors, regulators and other financial industry participants are attempting to understand the variability of loss projections, provision charges and Expected Credit Loss (ECL) estimates confirmed by GCD’s IFRS 9 benchmarking study. The study was conducted in the summer of 2018 over 26 international banks and supported banks in finalizing their IFRS 9 implementation.
CECL Benchmarking Survey 2019
CECL requires a complex set of methological choices. Accenture, Global Credit Data, and the Institute of International Finance partnered to provide U.S. banks with a benchmark to help them assess their readiness to implement CECL. Step 1 was a detailed survey, to be downloaded here. The results offer insight into the challenges faced by the banks in the areas of data management, model development, and technology/ implementation. Step 2 is a benchmarking study.
LGD Report Large Corporates 2019
This is the GCD annual Report on Loss Given Default (LGD) for Large Corporate, in which numerical evidence of recoveries and losses is presented. The data set covers Large Corporate (>€50m turnover) borrowers who are recorded as defaulted in bank loan books, using the Basel default definition.
GCD’s data pools support the key parameters of banks’ credit risk modelling (PD, LGD, EAD). This report covers LGD and represents a unique resource for all types of credit risk modelling: regulatory capital; pricing; stress testing; or expected loss provisioning models.
LGD Report Large Corporates 2018
Recovery rate and its inverse, Loss Given Default (LGD), is a key metric in credit risk modelling, whether for regulatory capital, pricing models, stress testing or expected loss provisioning models.
This report is the first time GCD publishes such extensive analytics on its broad data set. The aim is to present the numerical evidence of recoveries and losses experienced by banks when providing credit facilities to large corporate counterparties. The data set in the report covers Large Corporate (>€50m turnover) borrowers who are recorded as defaulted in bank loan books, using the Basel default definition.
Shipping Finance LGD Study 2017
The shipping industry is the backbone of global trade. More than almost any other sector, it benefits from globalization and economic upturn. This also makes the industry vulnerable to economic downturns.
GCD loss data confirms this general observation. This report based on the shipping finance loss data gives insights regarding four major questions:
- Does the data tell the story of why selling the ship is the option of last resort?
- What is the impact of collaterization on LGD?
- Can you link macroeconomic developments to the LGD curve over time?
- Is specialised lending actually riskier than corporate finance?
Downturn LGD Study 2017
Does loss given default (LGD) depend on the economic cycle and if so how can it be measured?
This question still concerns risk modellers and regulators as part of their comprehensive risk assessment. In 2013 GCD published a first downturn LGD study based on the GCD large-scale LGD database. This report provides an update of the analyses presented back then on a now significantly enlarged data set provided by over 50 member banks and covering over 15 years of default history.
Discount Rates for LGD Calculation 2016
This study provides a theoretical and empirical analysis of five alternative discount rate concepts for computing LGDs using GCD Data. Appropriate discount rates are based on a combination of the risk free rate and risk premium for systematic risk at the time of default. Approaches may be separated into contract specific, comparable and equilibrium approaches. Advantages and shortcomings are discussed in the paper.
The working group was chaired by Stephan Jortzik of ANZ and assisted by Harald Scheule, Associate Professor of Finance at the University of Technology, Sydney.
Project Finance Study 2014
Global Credit Data members have been working together for some years to improve and increase data on project finance cases. This study, based on 300 defaults as at June 2014, examines the different elements of project type, industry, location, etc. to see whether they are predictive of LGD outcome. There is also an interesting comparison between LGD rates observed for Project Finance loans vs unsecured Large Corporate loans.
The working group was chaired by Nina Brumma of KfW and assisted by Orla Duffy.
Default Rate Study 2013
Using both PD and default rate data collected from member banks in 2013, this paper examines cyclicality, averages, prediction vs outcome, accuracy rates and correlations.
The working group was chaired by Michel van Beest of NIBC and assisted by Jeroen Batema of OSIS.