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Title: Models of corporate and bank default and credit migration
Author: Dimou, Paraskevi
ISNI:       0000 0001 3424 6699
Awarding Body: City University London
Current Institution: City, University of London
Date of Award: 2007
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This thesis presents three studies on credit risk modelling. The first study compares the real default probabilities produced by three main structural models of default, Merton model, Longstaff and Schwartz model and Leland and Toft model, to the observed real default probabilities reported by Moody's for the BBB, BB and B rated bonds. We find that none of the models can accurately predict the default probabilities in all these cases. Merton as well as Leland and Toft models underpredict default probabilities. Longstaff and Schwartz model although it produces in some cases Expected Default Frequencies (EDFs) that are close to the observed ones, it tends to overestimate the default probabilities of riskier bonds as well as the default probabilities of bonds with the same rating but higher equity volatility. We also find that structural models tend to underestimate the default probabilities in early years. The second study examines whether information from equity markets, as summarized in the distance to default measure derived from a Merton-Moody's KMV (MKMV) model, provides useful additional information over accounting variables for predicting changes in bank credit ratings. Using a dataset of 98 equity listed banks from 1997 to 2004, we find that di~tance to default measure I has additional explanatory power for modeling current ratings, or predicting credit rating changes over a 6-month or l2-month horizon, but only for the smaller sized banks. We find no evidence that changes in distance-to-default have additional explanatory power for predicting rating categories, regardless of the size ofthe bank. The third study compares two proprietary models, Moody's KMV (MKMV) and BARRA models that use information from the equity and debt market respectively for the estimation of market implied ratings that can be updated continuously. We compare the empirical performance ofthese models in terms of their ability to predict in a timely fashion changes in credit quality by employing a sample of 4594 bonds issued by 447 firms from US for a period of3 years. We find that I?-either model provides a close mapping to observed ratings. Both however are useful for prediction of credit transitions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HG Finance