Submitted by Joao Moura (joaosic@gmail.com) on 2009-06-30T19:59:09Z No. of bitstreams: 1 Tese_MFEE_JOAO SICHIERI_2007.pdf: 336631 bytes, checksum: dc4d29066af0bd6c7564db0c209c3eb2 (MD5) / Approved for entry into archive by Vitor Souza(vitor.souza@fgv.br) on 2009-07-07T14:50:25Z (GMT) No. of bitstreams: 1 Tese_MFEE_JOAO SICHIERI_2007.pdf: 336631 bytes, checksum: dc4d29066af0bd6c7564db0c209c3eb2 (MD5) / Made available in DSpace on 2009-07-07T14:51:00Z (GMT). No. of bitstreams: 1 Tese_MFEE_JOAO SICHIERI_2007.pdf: 336631 bytes, checksum: dc4d29066af0bd6c7564db0c209c3eb2 (MD5) Previous issue date: 2007-05-30 / Credit risk management has assumed increasing importance for the managers and directors of enterprises. Thus, different approaches aimed to measure the probability of default are under discussion nowadays. This paper evaluates models that have become more popular over the last 30 years in order forecast defaults or to provide information regarding to financial difficulties of enterprises. This paper will focus on the KMV model in order to estimate the probability of default, its methodology based on market value of the asset and its volatility and finally estimate the probability of default. Finally, to test the KMV model will be used a sample of global steel companies that have credit in Companhia Vale do Rio Doce (CVRD), which will allow us to make comparisons with the models presented in this work.