GCD's Mission is to help banks understand and model credit risks. The comprehensive data pools are collected over a decade and distributed back to members for their own research and modelling.

 

Learn More

GCD is a unique data consortium that owns banks internal data for both PD and LGD. GCD’s data pools support the key parameters of banks’ credit risk modelling: Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD).

Learn More

GCD’s library gives access to wide variety of publications on risk related topics. Global Credit Data members work together to analyse the data and discuss methodology issues. GCD has published numerous papers and is actively promoting academic research on the data collected.

Access the Library 

Members not only benefit from exclusive rights and access to credit databases and analytics, but also from knowledge and research facilitation possible via the unique industry association.

Through a variety of forums such as workshops, webinars and surveys, GCD is an active industry participant facilitating the discussion in key strategic areas.

Learn More

Global Credit Data collects raw data from its members and distributes it back to them for use in their own analysis and modelling. GCD supports its members by providing a flexible high-end tool on the data pool: the GCD Visual Analyzer. Member banks can create dynamic Reference Data Sets and generate instant views on the data.

Learn More

-> menu code <-

How Can We Help?

Search for answers or browse our knowledge base.

< All Topics

PD Report Large Corporates 2023

Description:

This Probability of Default (PD) report covers a reference data set of 85,000 large corporate exposures, provided by 28 banks.

At the pool level, the Through-The-Cycle Probability of Default (TTC PD) is stable over time with a value of around 0.2% on investment grades and 3% on speculative grades, overall at 1.94%.  It is consistently above the observed Default Rate due to regulatory buffer requirements.

The insights gained from these high-level analyses confirm the benefits of detailed and granular collection of data – critical for banks using data-driven credit risk modelling to understand and quantify PD.

Author


Tags: