Macro stress testing of credit risk focused on the tails

Abstract

a b s t r a c tThis paper investigates macro stress testing of system-wide credit risk with special focus on the tails of the credit risk distributions conditional on adverse macroeconomic scenarios. These tails determine the ex-post solvency probabilities derived from the scenarios. This paper estimates the macro-credit risk link by the traditional Wilson (1997a,b) model as well as by an alternative proposed quantile regression (QR) method (Koenker and Xiao, 2002), in which the relative importance of the macro variables can vary along the credit risk distribution, conceptually incorporating uncertainty in default correlations. Stress-testing exercises on the Brazilian household sector at the one-quarter horizon indicate that unemployment rate distress produces the most harmful effect, whereas distressed inflation and distressed interest rate show higher impacts at longer periods. Determining which of the two stress-testing approaches perceives the scenarios more severely depends on the type of comparison employed. The QR approach is revealed more conservative based on a suggested comparison of vertical distances between the tails of the conditional and unconditional credit risk cumulative distributions.

A quantile regression approach to bank efficiency measurement*

“…This analysis is particularly useful when the conditional distribution does not have a standard shape, such as an asymmetric, fat-tailed, or truncated distribution. Consequently, quantile regression was recently employed in various strands of the finance and banking literature, including banking risk and regulations (Klomp and de Haan, 2012), the herding behavior in stock markets (Chiang et al, 2012), capital structure (Fattouh et al, 2005), bankruptcy prediction (Li and Miu, 2010), ownership and profitability (Li et al, 2009), the relationship between stock price index and exchange rate (Tsai, 2012), and credit risk (Schechtman and Gaglianone, 2012). 1 In the context of our study, quantile analysis provides an ideal tool to examine bank efficiency heterogeneity, departing from conditional-mean models.…”

Modelo de Defasagem Distribuída Polinominal para Teste de Estresse do Sistema Financeiro no Brasil

“…Os primeiros trabalhos que estudaram o risco de crédito em cenários macroeconômicos adversos foram publicados por Wilson (1998). Desde então, diversos estudos têm aplicado ferramentas de teste de estresse para avaliar a vulnerabilidade de sistemas bancários em cenários macroeconômicos adversos (KALIRAI; SCHEICHER, 2002;SCHECHTMAN;GAGLIANONE, 2012;SORGE;VIROLAINEN, 2006;VAN DEN END;HOEBERICHTS;TABBAE, 2006;VIROLAINEN, 2004;VLIEGHE, 2001).…”

“…As variáveis taxa de desemprego da Região metropolitana no Brasil, taxa de juros Selic e PIB acumulado dos últimos 12 meses foram significativas para prever a inadimplência, sendo que: (i) quanto maior o desemprego, maior a inadimplência. Uma maior taxa de desemprego pode causar um menor poder aquisitivo das famílias SCHECHTMAN;GAGLIANONE, 2012); (ii) quanto maior a taxa de juros, menor a inadimplência. Uma baixa taxa de juros pode acarretar um aumento da inflação, que pode causar um menor poder aquisitivo das famílias, comprometendo mais sua renda e aumentando a inadimplência (PESARAN et al, 2005;VAN DEN END;HOEBERICHTS;TABBAE, 2006); (iii) quanto maior o PIB, menor a inadimplência, indicando maior atividade econômica do país (FANG-YING, 2011;FUNGÁČOVÁ;JAKUBÍK, 2013).…”

“…Uma baixa taxa de juros pode acarretar um aumento da inflação, que pode causar um menor poder aquisitivo das famílias, comprometendo mais sua renda e aumentando a inadimplência (PESARAN et al, 2005;VAN DEN END;HOEBERICHTS;TABBAE, 2006); (iii) quanto maior o PIB, menor a inadimplência, indicando maior atividade econômica do país (FANG-YING, 2011;FUNGÁČOVÁ;JAKUBÍK, 2013). SCHECHTMAN;GAGLIANONE, 2012) e (ii) quanto maior a taxa de juros, menor a inadimplência (PESARAN et al, 2005;VAN DEN END;HOEBERICHTS;TABBAE, 2006). O quarto modelo estimado foi o modelo de dados em painel com efeitos fixos.…”

The financial crisis of 2008, credit markets and effects on developed and emerging economies

“…Nofsinger's analysis complements existing studies on the effects of behavioral biases on economic decisions by suggesting that these biases, if driving the behavior of sufficiently many agents, can contribute to systemic fragility and, equally importantly, make it difficult for policy makers to manage and resolve crises efficiently. Schechtman and Gaglianone (2012) contribute to literature on stress testing by examining the tails of credit risk distributions conditional on adverse macroeconomic scenarios. The paper estimates the macro-credit risk link by the traditional (Wilson, 1997a,b) model and a proposed quantile regression (QR) (Koenker and Xiao, 2002) which explicitly models the tail of the conditional distributions.…”