Credit Risk Determinants in the Vulnerable Economies of Europe: Evidence from the Spanish Banking System

Gila-Gourgoura, Esida; Nikolaidou, Eftychia

doi:10.25103/ijbesar.101.08

Cited by 4 publications

(8 citation statements)

References 35 publications

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“…Our findings suggest that unemployment and inflation are strong determinants of UK banking credit risk. This study empirically supported the findings of the existing literature on the influence of macroeconomic factors on credit risk, with a demonstration of a direct (positive) association between credit risk and inflation [17], the unemployment rate [13], and national savings [20]; an inverse (negative) relationship was established between credit risk and national debt [2], total trade deficit [29], and national income [19]. This paper significantly contributes to the empirical proof that the positive link between national savings and NPLs-that is, the "Paradox of Thrift"-is as follows: when savings rise, national wealth rises owing to a failure to spend money within the market, which slows the economy and impacts supply-demand (trade deficit), which in turn decreases GDP and enhances credit risk.…”

Section: Discussionsupporting

confidence: 86%

“…The findings of all three regression model equations reinforce the existing literature on macroeconomic factors' impact on credit risk. Conclusively, regression models demonstrate a positive link between credit risk and inflation [17], the unemployment rate [13], and national savings [20], as well as a negative link with the UK's national debt [2], total trade deficit [29], and national income [19].…”

Section: Logistic Regressionmentioning

confidence: 94%

“…The impact of trade deficit on credit risk has not received much attention [29]; thus, there is a need to examine the effects of the UK's trade deficits on NPLs.…”

Section: Research Gap Proposed Solutionmentioning

confidence: 99%

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Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector

Sharma,

Andhalkar,

Ajao

et al. 2024

Analytics

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Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive models (traditional statistical and machine learning algorithms), this study aims to examine the macroeconomic determinants’ impact on the UK banking credit risk and assess the most accurate credit risk estimate using predictive analytics. This study found that the variance-based multi-split decision tree algorithm is the most precise predictive model with interpretable, reliable, and robust results. Our model performance achieved 95% accuracy and evidenced that unemployment and inflation rate are significant credit risk predictors in the UK banking context. Our findings provided valuable insights such as a positive association between credit risk and inflation, the unemployment rate, and national savings, as well as a negative relationship between credit risk and national debt, total trade deficit, and national income. In addition, we empirically showed the relationship between national savings and non-performing loans, thus proving the “paradox of thrift”. These findings benefit the credit risk management team in monitoring the macroeconomic factors’ thresholds and implementing critical reforms to mitigate credit risk.

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Section: Discussionsupporting

confidence: 86%

Section: Logistic Regressionmentioning

confidence: 94%

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Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector

Sharma,

Andhalkar,

Ajao

et al. 2024

Analytics

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show abstract

“…Before estimating the stationarity test, the length of the lag must be determined, and a bound test must be performed. The first condition for using the ARDL method is that the data must be stationary at the level or stationary at the first difference and it is better not to suggest being stationary at the second difference only (Gourgoura & Nikolaidou, 2017).…”

Section: Methodsmentioning

confidence: 99%

Do Interest Rate Policy and Liquidity Effect on Banking Credit Risk in Indonesia?

Amalia

Suriani

2023

Signifikan

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Lending plays a vital role for banks as a source of income from deposits or interest paid by debtors. This study aims to analyze the effect of policy interest rates and liquidity from the money supply on bank credit risk in Indonesia in the short and long term. This study uses the Autoregressive Distributed Lag method and the Granger Causality test as analytical tools. The data used are policy interest rates, total money supply, and total non-performing loans. The data period under study is 2017-2022. The study results show that in the short term, policy interest rates and the money supply negatively affect bank credit risk in Indonesia. However, in the long term, policy interest rates have a negative effect, and the money supply does not affect bank credit risk in Indonesia. Policy interest rates have a one-way causality relationship with bank credit risk. Meanwhile, bank credit risk has a one-way causality relationship to the money supply. This condition represents that policy interest rates can reduce bank credit risk in Indonesia. The Bank of Indonesia, as the monetary authority, needs to pay attention to fluctuations in policy interest rates and mitigate excess money supply so that credit risk does not increase.JEL Classification: F43, O11, P34How to Cite:Amalia, S. Q., & Suriani, S. (2023). Do Interest Rate Policy and Liquidity Affect Banking Credit Risk in Indonesia?. Signifikan: Jurnal Ilmu Ekonomi, 12(1), 145-160. https://doi.org/10.15408/sjie.v12i1.27119.

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“…Consistently, Nikolaidou and Vogiazas (2014) find that NPLs in the Bulgarian banking system are explained by both macroeconomic and industry-specific variables as well as by exogenous factors such as the recent global financial crisis. Gila-Gourgoura and Nikolaidou (2016), conclude that the real GDP, the Spanish long-term government bond yield, the return on equity, the total credit granted by the Spanish banks and their capital to assets ratio, explain credit risk in Spain both in the short and the long run.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Determinants in the Vulnerable Economies of Europe: Evidence From the Italian Banking System

Gila-Gourgoura¹,

Nikolaidou²

2018

Proceedings of the 10th Economics &Amp; Finance Conference, Rome

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This study uses the ARDL approach to cointegration to identify the factors affecting credit risk in the Italian banking system over the period 1997Q4-2017Q1. The ratio of new bad loans to the outstanding amount of performing loans in the previous period is the selected proxy of credit risk whereas a wide range of explanatory variables are included in the study. Compared to the previous studies, a wider timeframe is investigated, which captures the booming period, the global financial crisis and the ongoing Eurozone sovereign debt crisis. The findings suggest that macroeconomic cyclical, bank-specific, and financial market variables affect the flow of new bad loans in the Italian banking system. The high significance of the sovereign debt crisis risk proxy signals the important link between banking and sovereign debt crisis.

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Credit Risk Determinants in the Vulnerable Economies of Europe: Evidence from the Spanish Banking System

Cited by 4 publications

References 35 publications

Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector

Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector

Do Interest Rate Policy and Liquidity Effect on Banking Credit Risk in Indonesia?

Credit Risk Determinants in the Vulnerable Economies of Europe: Evidence From the Italian Banking System

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