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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Aleksandr Kuz`min
Estimating Credit Risk of Public Companies
Master’s programme
10
2014
This paper is dedicated to analyzing major credit risk models applicable to public companies and to determining factors effecting their probability of default. Market and financial data on issuers traded on Moscow Exchange for the period from 2007 to 2014 is used. Three major approaches to estimating probability of default examined: reduced-form, structural and fundamental data models. Taking into account the absence of a default statistics for the Russian public companies a tailored approach to applying the Merton model and fundamental models was proposed. As a result a binary model for classifying companies as those with high risk or low risk of default has been developed

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