A taxa de recuperação de créditos ruins em bancos comerciais privados brasileiros

• Ano de defesa: 2004
• Autor(a) principal: Araújo, Evaristo Donato
• Orientador(a): Sicsú, Abraham Laredo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Administração contábil e financeira; Inadimplência; Taxa de recuperação
• Palavras-chave em Inglês: Recovery rate; Default rate; Loss given default; Commercial banks
• Link de acesso: https://hdl.handle.net/10438/2457

Credit risk comes from the possibility of the debtor not paying its debt at the maturity date, and the promised amount. When the debtor doesn’t pay in full its debt, we say he or she is in default. In this case, the creditor gets a loss. However, the loss could be reduced if the debtor pays part of his or her debt. The measurement of a debtor’s probability of default has been the subject of studies for decades. However, the measurement of how much one can receive from a defaulted credit – the recovery rate – has been given attention only recently. And, most of the time, this measure has been calculated for huge companies in United States financial markets, only. We have defined recovery rate based on financial reports of Brazilian commercial banks, and tracked the path of this variable pari passu to default rate, defined from the same reports also. We established a theoretical framework, and made hypothesis on how such variables as default rates and other credit quality indicators, economic level indicators, nominal and real interest rates, and capital markets indicators could explain variations on the recovery rates we have defined. We gathered information from 46 Brazilian private commercial banks, semiannually, bracing the period between June of 1994 and December 2002. These institutions were segmented by their share on the amount of credit of the private banking industry in Brazil and by the origin of its capital. Statistical models were run on explanatory variables based on original data and on variables obtained from principal components analysis. The models were able to explain most of the variation observed on the recovery rate we have defined, for the segments we have studied. The best models have shown that variations on the recovery rate could be explained by default rates and other indicators of credit quality, economic activity indicators and capital markets indicators.