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Economists show that macroeconomic conditions affect credit risk of financial institutions. Thus, there is a need to evaluate the sensitivity of credit risk of financial institutions to changes in the economy in order to avoid instability in the credit market. This work contributed to the analysis of credit risk by presenting a prediction model for non-performing loans of Brazilian financial system using a comprehensive set of macroeconomic variables in the analysis. The model also incorporates the composition of the loan portfolio of financial institutions. The literature review used, as a construct, empirical studies in sovereign credit risk area, stress testing, credit scoring with macroeconomic variables, studies that relate nonperforming loans and economic variables and studies relating to credit risk and composition of the loan portfolio. The monthly data base was extracted from Central Bank and IPEA data. The response variable of the model, the non-performing loans rate is defined as the ratio between the outstanding balance more than ninety days of credit agreements and the balance of all contracts in the data base. 313 explanatory variables were extracted based on the literature review. Statistical models of time series (ARIMA) and time series with exogenous explanatory variables (ARMAX) were built to predict non-performing loans. The modeling database used the period between March 2007 to December 2011. The period from January 2012 to December 2012 was used to measure the performance of the models out of time (validation sample). Explanatory variables by region of the loan portfolio, the internal public debt and the interest rate were statistically significant in explaining the default of the financial system in Brazil , where (1) the higher the annual growth of loans in Centro-Oeste, the lower the non-performing loans, (2) the higher average interest rates applied by the Central Bank in the last three months, the lower the non-performing loans and (3) the higher annual growth of domestic debt of the public sector, lower nonperforming loans. Comparing the obtained models, ARIMA model showed better fit for the year 2012, but ARMAX model also performed properly with low residual values. Sumário 1. INTRODUÇÃO  5 2. REVISÃO BIBLIOGRÁFICA  9 2.1. Risco de Crédito Soberano  10 2.2. Teste de Estresse  15 2.3. Modelos de Credit Scoring com variáveis macroeconômicas  21 2.4. Estudos sobre Inadimplência e Macroeconomia  27 2.5. Composição da carteira de crédito  33 3. METODOLOGIA  36 3.1. Tipo de estudo  36 3.2. Base de dados  37 3.3. Séries temporais  39 3.3.1. Séries estacionárias  40 3.3.2. Modelo ARIMA  41 3.3.3. Modelo SARIMA  43 3.4. Modelo ARIMAX  43 3.5. Transformações  44 3.5.1. Médias Móveis de 3 meses  44 3.5.2. Variação Interanual  45 3.5.3. Defasagem  45 3.6. Seleção inicial das variáveis explicativas  46 3.6.1. Correlação  46 3.6.2. Teste de Causalidade de Granger  46 3.6.3. Co-integração  46 3.6.4. Exogeneidade  47 4. ANÁLISE E RESULTADOS  49 4.1. Modelo de séries temporais ARIMA  49 4.2. Modelo de séries temporais com variáveis explicativas ARMAX  54 4.2.1. Seleção de variáveis  55 4.2.2. Modelo ARMAX  61 4.3. Comparativo  69 5. CONSIDERAÇÕES FINAIS  70 REFERÊNCIAS  72 APÊNDICES  78

A inadimplência do sistema financeiro no Brasil explicada por meio de fatores macroeconômicos