مدل آزمون استرس کلان ریسک اعتباری برای بخش بانکداری برزیل

This paper proposes a model to conduct macro stress test of credit risk for the banking sector based on scenario analysis. We employ an original bank-level data set that splits bank credit portfolios in 21 granular categories, covering household and corporate loans. The results corroborate the presence of a strong procyclical behavior of credit quality, and show a robust negative relationship between the logistic transformation of non-performing loans (NPLs) and GDP growth, with a lag response of up to three quarters. The results also indicate that the procyclical behavior of loan quality varies across credit types. This is novel in the literature and suggests that banks with larger exposures to highly procyclical credit types and economic sectors would tend to undergo sharper deterioration in the quality of their credit portfolios during an economic downturn. Lack of sufficient portfolio granularity in macro stress testing fails to capture these effects and thus introduces a source of bias that tends to underestimate the tail losses stemming from the riskier banks in a system.