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Prediction of bank rating transition probabilities

Listed author(s):
  • Paraskevi Dimou
  • Alistair Milne
  • Francesca Campolongo

The objective of this paper is to determine whether information from equity markets, as summarized in the distance to default measure derived from Merton-MKMV, is useful for modeling and predicting bank credit ratings. We use the BankScope database and Bloomberg to build a data set comprising of 98 equity listed banks, from 8 English-speaking and Scandinavian countries, with annual accounting and daily ratings and equity price data from 1997 to 2004. We divide bank ratings into four broad credit risk classes. We then build an ordered-probit model of the current credit rating class, incorporating both accounting ratios and a Merton-type measure of distance to default. We find distance to default has no additional explanatory power for modeling current ratings, or predicting credit rating changes over a 6-month or 12-month horizon. We find some evidence that changes in distance-to-default, have some additional explanatory power for changes in ratings

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 520.

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Date of creation: 04 Jul 2006
Handle: RePEc:sce:scecfa:520
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