Please update your browser

You're using a version of Internet Explorer that isn't supported by Questia.
To get a better experience, go to one of these sites and get the latest
version of your preferred browser:

Modeling Ratings Migration for Credit Risk Capital and Loss Provisioning Calculations

By Sobehart, Jorge; Keenan, Sean | The RMA Journal, October 2004 | Go to article overview

Modeling Ratings Migration for Credit Risk Capital and Loss Provisioning Calculations


Sobehart, Jorge, Keenan, Sean, The RMA Journal


Reliable loss prediction requires both robust estimation methods and accurate data. This article presents a way to leverage ratings agency data that can provide greater flexibility and stability of results in simulation-based estimates of future portfolio losses.

Based on a simple behavioral model that quantifies the structural relationships in historical default frequencies and transition rates for different ratings, (1) this technique leads analysts to hypothetical transition matrices for portfolio loss simulations that preserve the basic relationships observed in the historical transition and default rates reported by the ratings agencies, allowing for unlimited sampling. The matrices can also be linked to macroeconomic factors to mimic the dynamics of credit cycles and economic shocks, allowing for richer descriptions of plausible future scenarios and what-if scenario analysis that goes beyond the limitations of historical data.

The Basel II capital adequacy framework provides strong incentive for financial institutions to use internal risk management systems to measure risk and determine sufficient regulatory and economic risk capital. While commercial risk measurement tools can be used as part of an overall solution, institutions must tailor them to their own portfolio specifications. Further, some of the development and implementation of the new systems will fall to their own risk management teams.

In many cases, whether they use commercial models or internal methodologies, analysts continue to rely on data from the major ratings agencies for default rates, ratings migration rates, and other key statistics. Despite recurring and somewhat troubling issues regarding the meaning and consistency of ratings, regulators tend to be more accepting of methodologies based on agency data because of the agencies' long and well-documented ratings histories. This data may indeed be deeper and may conform better to an accepted standard than banks' own internal ratings histories, yet the depth of agency data generally falls short of what's needed for the Monte Carlo-based economic risk capital estimation techniques in widespread use today.

The Shortcomings

The simplest portfolio loss model assumes that ratings transition probabilities are stable across obligor types and across the business cycle, and that a single set of average historical ratings transition and default rates is all that's needed to characterize potential future losses. However, there is ample evidence that credit migration and the ratings process depend on a number of factors, such as the state of the economy-for example, the probability of downgrades and defaults is greater in a downturn than in an upturn. Moreover, historical data is volatile; thus, the average-rate approach will understate potential tail loss--the very thing we want to measure with precision. A slightly more sophisticated alternative is to use observed annual historical-rating transition rates as a sample from which to draw plausible future credit migration scenarios to simulate the forward loss distribution. The main drawback of this method is the small number of historical-rating scenarios on which to draw. Accurate Monte Carlo simulations for large portfolios usually require tens--or even up to hundreds of thousands--of random draws. However, because historical scenarios number only in the tens, the simulated loss distribution will tend to be lumpy as tail losses bunch up around the worst year from the historical period. Clearly, this problem cannot be overcome by increasing the number of Monte Carlo simulations.

A Behavioral Model of Risk Perception

A different approach is to directly model the relationship between transition probabilities and macroeconomic factors and then simulate plausible ratings migration patterns over time by generating various macroeconomic conditions. To do this, we need a behavioral model of how risk ratings are assigned.

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

Modeling Ratings Migration for Credit Risk Capital and Loss Provisioning Calculations
Settings

Settings

Typeface
Text size Smaller Larger
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Full screen

matching results for page

Cited passage

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited passage

Welcome to the new Questia Reader

The Questia Reader has been updated to provide you with an even better online reading experience.  It is now 100% Responsive, which means you can read our books and articles on any sized device you wish.  All of your favorite tools like notes, highlights, and citations are still here, but the way you select text has been updated to be easier to use, especially on touchscreen devices.  Here's how:

1. Click or tap the first word you want to select.
2. Click or tap the last word you want to select.

OK, got it!

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.