Ratings Shopping and Asset Complexity: A Theory of Ratings Inflation

31 Pages Posted: 26 Feb 2009 Last revised: 1 Nov 2021

See all articles by Vasiliki Skreta

Vasiliki Skreta

University of Texas at Austin - Department of Economics; University College London

Laura Veldkamp

Columbia University - Columbia Business School; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: February 2009

Abstract

Many identify inflated credit ratings as one contributor to the recent financial market turmoil. We develop an equilibrium model of the market for ratings and use it to examine possible origins of and cures for ratings inflation. In the model, asset issuers can shop for ratings -- observe multiple ratings and disclose only the most favorable -- before auctioning their assets. When assets are simple, agencies' ratings are similar and the incentive to ratings shop is low. When assets are sufficiently complex, ratings differ enough that an incentive to shop emerges. Thus, an increase in the complexity of recently-issued securities could create a systematic bias in disclosed ratings, despite the fact that each ratings agency produces an unbiased estimate of the asset's true quality. Increasing competition among agencies would only worsen this problem. Switching to an investor-initiated ratings system alleviates the bias, but could collapse the market for information.

Suggested Citation

Skreta, Vasiliki and Veldkamp, Laura, Ratings Shopping and Asset Complexity: A Theory of Ratings Inflation (February 2009). Available at SSRN: https://ssrn.com/abstract=1349593

Vasiliki Skreta (Contact Author)

University of Texas at Austin - Department of Economics ( email )

Austin, TX 78712
United States

HOME PAGE: http://vskreta.wixsite.com/vskreta

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

HOME PAGE: http://vskreta.wixsite.com/vskreta

Laura Veldkamp

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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