Credit risk and the business cycle: What do we know?

Georgios Chortareas; Georgios Magkonis; Kalliopi Maria Zekente

doi:10.1016/j.irfa.2019.101421

Credit risk and the business cycle: What do we know?

Georgios Chortareas, Georgios Magkonis^*, Kalliopi Maria Zekente

^*Corresponding author for this work

Economics

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

Abstract

We perform a meta-regression analysis to characterize the relationship between ex post credit risk, measured through non-performing loans and real GDP growth. Although the prior empirical literature reveals a statistically significant inverse association, the precise effect of growth performance to credit quality diverges and remains subject to several qualifications. Using estimates from 56 studies and applying a Bayesian meta-regression analysis we explore the systematic patterns of the heterogeneity in the reported estimates. According to our evidence, the specification form as well as features related to the type of data, and the sample period are factors that systematically influence the estimated results.

Original language	English
Article number	101421
Journal	International Review of Financial Analysis
Volume	67
DOIs	https://doi.org/10.1016/j.irfa.2019.101421
Publication status	Published - 1 Jan 2020

Keywords

Business cycles
Credit risk
Macro-stress testing
Meta-analysis
Non-performing loans

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10.1016/j.irfa.2019.101421

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title = "Credit risk and the business cycle: What do we know?",

abstract = "We perform a meta-regression analysis to characterize the relationship between ex post credit risk, measured through non-performing loans and real GDP growth. Although the prior empirical literature reveals a statistically significant inverse association, the precise effect of growth performance to credit quality diverges and remains subject to several qualifications. Using estimates from 56 studies and applying a Bayesian meta-regression analysis we explore the systematic patterns of the heterogeneity in the reported estimates. According to our evidence, the specification form as well as features related to the type of data, and the sample period are factors that systematically influence the estimated results.",

keywords = "Business cycles, Credit risk, Macro-stress testing, Meta-analysis, Non-performing loans",

author = "Georgios Chortareas and Georgios Magkonis and Zekente, {Kalliopi Maria}",

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AU - Magkonis, Georgios

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AB - We perform a meta-regression analysis to characterize the relationship between ex post credit risk, measured through non-performing loans and real GDP growth. Although the prior empirical literature reveals a statistically significant inverse association, the precise effect of growth performance to credit quality diverges and remains subject to several qualifications. Using estimates from 56 studies and applying a Bayesian meta-regression analysis we explore the systematic patterns of the heterogeneity in the reported estimates. According to our evidence, the specification form as well as features related to the type of data, and the sample period are factors that systematically influence the estimated results.

KW - Business cycles

KW - Credit risk

KW - Macro-stress testing

KW - Meta-analysis

KW - Non-performing loans

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VL - 67

JO - International Review of Financial Analysis

JF - International Review of Financial Analysis

M1 - 101421

ER -

Credit risk and the business cycle: What do we know?

Abstract

Keywords

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