The determinants of bank stability: Evidence from selected Balkan countries and Turkey

Alihodžić, Almir; Ekşi̇, İbrahim Halil; Doğan, Berna

doi:10.5937/intrev2001094a

Cited by 10 publications

(7 citation statements)

References 40 publications

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“…Aksoy and Donduran (2019) found a positive relationship between competition and bank stability using data for the Turkish banking sector from 2006-2016. Alihodzic et al (2020) investigated the determinants of bank stability for Turkey and six other Balkan countries using the Z-score and NPL as dependent variables. This study reported that GDP, cost to income ratio, the net interest margin and the Lerner index had the highest correlation with the Z-score.…”

Section: Theme 2: Z-score Applications As a Bank Stability Measurementioning

confidence: 99%

Bank Stability and Systemic Risk Measurement

Tarkocin,

Donduran

2023

IJFBS

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The banking system’s role as an intermediary between depositors and borrowers makes its stability a crucial element of the economy. Individual bank stability impacts the stability of the financial sector and, consequently, the real sector and economic growth. This study is a detailed investigation of bank stability scores for the Turkish banking sector. Turkey experienced a major banking crisis from 2000 to 2001. However, with an improved regulatory environment and structural reforms, its resilience to shocks increased significantly in subsequent years. The present study contributes to the literature in three areas. First, it applies eight variations of the Z-score to the Turkish banking sector to test the importance of time and risk variation. Second, it investigates the impact on stability scores of several factors–the implementation of new capital regulations and Basel III liquidity rules, the Turkish lira crisis and the effects of COVID-19). Finally, it measures the contribution to systemic risk of each Turkish bank using the leave-one-out (LOO) Z-score method; this is the first study to use this method to analyse the contribution of different bank types to the overall stability of the sector.

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Section: Theme 2: Z-score Applications As a Bank Stability Measurementioning

confidence: 99%

Bank Stability and Systemic Risk Measurement

Tarkocin,

Donduran

2023

IJFBS

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“…The risk quantification of rating categories using default probability magnitudes has the potential to contribute to different rating methodologies that are less relevant in theory, and settlement will be possible using these default probability values as a kind of "common currency". [2] Ratings are also used as a basis for economic capital allocation decisions and as inputs into sophisticated measurement, portfolio management and pricing applications. Almost all banks rely on the rating when preparing summary reports for senior management in order to monitor the composition of risks in the assessed portfolios.…”

Section: Risk Factors In Determining Client Ratingsmentioning

confidence: 99%

The role of the banks' rating system in the allocation of loans

Šegrt,

Vučinić,

Mihajlović

et al. 2023

International Review

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The subject of this paper is the analysis of the application of banking internal credit risk measurement models for the purpose of calculating the minimum regulatory capital. The Basel Committee established proposals for an internal rating-based approach (IRB approach-internal rating-based) to capital requirements for credit risk. Such an approach, which relies on the bank's internal assessment of counterparties and exposures, can ensure two key objectives: the first is additional risk sensitivity, in which capital requirements based on internal ratings can be much more sensitive on the drivers of credit risk of economic losses in the banking portfolio; the second is incentive compatibility, where the appropriate structure of the RBI approach can provide a framework that encourages banks to continue to improve their internal risk management practices. The internal ranking approach aims to improve the safety and soundness of the financial system. The paper defines the terminology and classification of the rating system. The probability of default (PD-probability at default) and the other two risk components LGD (loss given default) and EAD (exposure at default) are key input parameters for the calculation of regulatory capital. The rating system is, therefore, a significant driver of risk management and financial performance measurement. To be in a position to demonstrate to supervisors that an internal rating system should be used for the purposes of determining minimum regulatory capital requirements, banks must first demonstrate that the rating system is an integral part of their ongoing operations and risk management culture.

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“…Beck et al [7], conclude that small companies and companies in states with adequate institutions are less likely to reach for external financing sources, notably loans from banks [8]. Therefore, the BS is the bloodstream of the economic system of the developed economy [9], because it is the banks that ensure the transfer of capital between the real sectors of the national economy and have a central role in payments [10] [11] [12]. A large increase in liquidity demand hit banks in March 2020.…”

Section: Indroductionmentioning

confidence: 99%

What can be expected in credit-risk management from NPL in the western Balkans region in the future?

Barjaktarović¹,

Vesić²,

Laki³

2022

International Review

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This paper presents an analysis of the impact of the rate of selected parameters on the banking system performance, specifically to non-performing loans (NPLs) movements. The goal is to investigate which the most influential factors affecting the movement of NPLs in the WB countries are, given that research have pointed out the impact of macroeconomic factors on the formation of NPL rates in banking systems. The authors have added several parameters to their methodology, dividing the indicators into internal and external. On the topic of indicators that affect the performance of the banking system, but also predictions of future trends of NPLs, several hypotheses can be set, but this research will start from the hypothesis that the NPL trend can be predicted by creating predictive models which, as the basis, have a combination of macroeconomic and banking system performance indicators. In addition to scientific literature, publications of international development and those by financial institutions were used as well, and the authors also accessed the international database - CEIC data. The time aspect of the research will cover the period 2010-2019, and the prediction of NPL trends will be performed for the period 2020-2025. To determine the final model and the indicator that will most accurately describe the target variable, the Merton's model in the statistical tool R will be developed and prediction tests will be fey performed. The most important statistical methods: linear regression, R2, ADJ R2, correlation matrix. The results show that in 3 out of 5 observed indicators, the one that most influences the trend of problem loans is the unemployment rate. Based on the modelling, the outputs indicate small deviations between the NPL obtained by the model and the publicly announced NPL-trends are very well presented, and the forecast results indicate a sharpening of the NPL trend curve in the period up to 2025. The contribution of this paper is reflected in the time prediction of NPL trends which can be useful to state authorities for adequate measure implementation.

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The determinants of bank stability: Evidence from selected Balkan countries and Turkey

Cited by 10 publications

References 40 publications

Bank Stability and Systemic Risk Measurement

Bank Stability and Systemic Risk Measurement

The role of the banks' rating system in the allocation of loans

What can be expected in credit-risk management from NPL in the western Balkans region in the future?

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