Correlated defaults: let’s go back to the data

Estimates of asset value correlation are a key element of Merton-style credit portfolio models. Many practitioners have access to asset value data for a large universe of listed firms, so estimation is within reach. Alan Pitts describes a statistical approach to estimating asset value correlation that has several advantages: estimates are easy to assign to unlisted firms and are sensitive to firm size, and estimation is straightforward with standard statistical software

Click Here To Download PDF

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 copy this content. Please contact info@risk.net to find out more.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

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…

Most read articles loading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here