![Risk.net](https://www.risk.net/sites/default/files/styles/print_logo/public/2018-09/print-logo.png?itok=1TpHrpuP)
Confidence intervals for corporate default rates
Rating agency default studies provide estimates of mean default rates over multiple time horizons but have never included estimates of the standard errors of the estimates. This is due at least in part to the challenge of accounting for the high degree of correlation induced by their cohort-based methodologies. In this article, Richard Cantor, David Hamilton and Jennifer Tennant present a method for estimating confidence intervals for corporate default rates derived through a bootstrapping approach
Historical average cumulative default rates by rating category and investment horizon are among rating agencies' most widely referenced statistics. In any finite sample, however, the historical mean default rate may overstate or understate the underlying population's true risk of default, depending upon whether the particular set of issuers included in the sample happen to experience lower or higher than expected default incidence. Quantitative credit analysts and risk managers are, therefore
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe
You are currently unable to print this content. Please contact info@risk.net to find out more.
You are currently unable to copy this content. Please contact info@risk.net to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
More on Credit risk
Credit risk management solutions 2024: market update and vendor landscape
A Chartis report outlining the view of the market and vendor landscape for credit risk management solutions in the trading and banking books
Finding the investment management ‘one analytics view’
This paper outlines the benefits accruing to buy-side practitioners on the back of generating a single analytics view of their risk and performance metrics across funds, regions and asset classes
Revolutionising liquidity management: harnessing operational intelligence for real‑time insights and risk mitigation
Pierre Gaudin, head of business development at ActiveViam, explains the importance of fast, in-memory data analysis functions in allowing firms to consistently provide senior decision-makers with actionable insights
Sec-lending haircuts and indemnification pricing
A pricing method for borrowed securities that includes haircut and indemnification is introduced
XVAs and counterparty credit risk for energy markets: addressing the challenges and unravelling complexity
In this webinar, a panel of quantitative researchers and risk practitioners from banks, energy firms and a software vendor discuss practical challenges in the modelling and risk management of XVAs and CCR in the energy markets, and how to overcome them.
Credit risk & modelling – Special report 2021
This Risk special report provides an insight on the challenges facing banks in measuring and mitigating credit risk in the current environment, and the strategies they are deploying to adapt to a more stringent regulatory approach.
The wild world of credit models
The Covid-19 pandemic has induced a kind of schizophrenia in loan-loss models. When the pandemic hit, banks overprovisioned for credit losses on the assumption that the economy would head south. But when government stimulus packages put wads of cash in…
Driving greater value in credit risk and modelling
A forum of industry leaders discusses the challenges facing banks in measuring and mitigating credit risk in the current environment, and strategies to adapt to a more stringent regulatory framework in the future