How Micro Data Improve the Estimation of Household Credit Risk Within the Macro Stress Testing Framework

Ján Klacso

Computational Economics, 2024, vol. 64, issue 2, No 4, 707-733

Abstract: Abstract Macro stress testing has become an increasingly important part of central banks’ and macroprudential authorities’ toolkits in the wake of the Global Financial Crisis. One of the most important parts of the stress testing framework is the estimation of credit risk losses under adverse circumstances. However, standard satellite models based on econometrics of time series may not be well suited to estimate credit risk losses for countries with short time series, incomplete credit cycle or structural breaks, as in the case of Slovakia. Incorporating micro data into the stress testing framework becomes important for such countries. In this paper, we show that using micro data leads to more plausible results compared to time series in form of higher estimated share of nonperforming loans. For the estimations, we use a unique set of individual retail loan data collected from banks operating in Slovakia. As the Slovak banking sector did not face a rapid worsening of the credit quality of retail loans during the Global Financial Crisis, the recent Covid-19 pandemic in 2020 is the only reference period of increased economic and financial tensions. This period also confirms our approach, as using micro data leads to nonperforming loan ratios that are closer to the ratio of indebted households asking for loan payment deferral during the pandemic.

Keywords: Macro stress testing; Microdata; Household credit risk; Incomplete credit cycle (search for similar items in EconPapers)
JEL-codes: C58 G51 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10453-9

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