Credit risk cyclicality in Serbian banking sector

Introduction Three main variables affect the credit risk of a financial asset: (i) the probability of default (PD), (ii) the ‘loss given default’ (LGD), which is equal to one minus the recovery rate in the event of default (RR), and (iii) the exposure at default (EAD). While significant attention has been devoted by the credit risk literature on the estimation of the first component (PD), much less attention has been dedicated to the estimation of RR and to the relationship between PD and RR. This is mainly the consequence of two related factors. First, credit pricing models and risk management applications tend to focus on the systematic risk components of credit risk, as these are the only ones that attract risk-premia. Second, credit risk models traditionally assumed RR to be dependent on individual features (e.g. collateral or seniority) that do not respond to systematic factors, and therefore to be independent of PD. This traditional focus only on default analysis has been partly reversed by the recent increase in the number of studies dedicated to the subject of RR estimation and the relationship between the PD and RR (Fridson et al . 2000, Gupton et al . 2000, Altman et al . 2001, Altman et al . 2003, 2005, Frye 2000a, 2000b, 2000c, Hu and Perraudin 2002, Hamilton et al . 2001, Jarrow 2001 and Jokivuolle and Peura 2003). This is partly the consequence of the parallel increase in default rates and decrease of recovery rates registered during the 1999–2002 period.

Credit risk has become one of the main risks faced by banks under the background of increasing loan demand. Meanwhile, bank credit risk may be affected by a number of factors, including the expansion rate of Gross Domestic Product (GDP), changes in interest rate, unemployment rate and even more. These macroeconomic factors are crucial in determining how credit risk for banks is shaped. This paper concludes the different impacts of different macroeconomic factors discussed in several papers on the risks of bank credit, and illustrates how changes in GDP growth rate and rate of interest affect credit risks. Through comprehensive literature review and empirical research, this paper finds that macro factors, including the expansion rate of GDP and the rate of interest, have considerable impacts on banks' credit risk. Bank credit risk will decrease as GDP growth rate increases; however the risk of bank credit will increase as interest rates rise. At the end, the paper provides several suggestions for the development of risk management strategies, including the monitoring of macroeconomic factors and the adjustment of credit policies under different economic situations. The research results are intended to help researchers in related fields understand current research findings and patterns of development in the research field, to give a theoretical foundation and backing for their investigations, and to offer important guiding significance for banks and relevant regulators to formulate risk management strategies.

With the rapid development of social economy and information technology, the credit risk and financial risk of my country’s financial enterprises are also facing severe challenges. In financial enterprises, credit is related to the survival of the enterprise. As the business volume and scale of financial enterprises continue to expand, financial risks are correspondingly increased. Therefore, the research on financial enterprise credit and financial risks is of great significance. The research on the credit and financial risks of financial enterprises is helpful to help financial enterprises handle financial risks well and perform evasive operations on them. In addition, it can also enhance the credit awareness of enterprises and reduce the default rate in the financial industry. This paper studies and analyzes the financial enterprise credit and financial risk measurement based on the PSM model. First, it uses the literature method to study the PSM model, corporate credit, financial risk and other theoretical knowledge, and then establish a fuzzy neural network model for risk assessment. And the establishment of a PSM model to conduct a questionnaire survey experiment design, analyze the price sensitivity changes and acceptable price ranges under the PSM model, and get the optimal pricing of new financial products issued by financial companies. Finally, it analyzes the relationship between the default rate of corporate credit and internal finance. The conclusion is that when this financial product is priced at 45 yuan, the proportion of reserved recipients is the largest, reaching 66%; when the price is 75 yuan, the acceptable proportion is 23%, which is the acceptable number of people in the three price ranges. The proportion is the largest; if the price is 100 yuan, the unacceptable proportion is the largest, reaching 45%. This shows that the pricing of a new financial product is directly related to its sales. The reasonableness of the product pricing directly determines whether people are willing to pay for it and accept it.

Farming communities who borrow agribusiness credit from Agricultural Finance Corporation (AFC) require efficient access to credit. Lender behaviour and decisions exhibited in credit supply dynamics influence agribusiness loan default rate. Mount Kenya region has registered a high default rate of 20.33% compared to a standard of 10% for all types of loans in Kenya. Using descriptive research design this, study sought to assess the effect of lender behavioural characteristics on agribusiness loans default rate in the region’s 11 branches and a population of 3,002 agribusiness borrowers. Systematic random sampling technique with an interval of 10 was used to sample 300 respondents. Primary data on lender behavioural characteristics was collected using a structured questionnaire. Statistical Packages for Social Sciences (SPSS V.27) and Stata version 15 were used to analyse data. To estimate the effect of variables in predicting default rate, regression analysis was used. In obtainment of the F-statistic for measuring the adequacy of the regression model, ANOVA was performed. Probit regression model was used to specify the statistical relationship between the variables. The four indicators used in the model explicated 48.80% of the dependent variable. Lender behavioural characteristics that were considered in the study had significantly affected AFC loan default rate at 1% level. The p-values of the pointers of lender behavioural characteristics for farm visit, disbursement timeliness, political lending and adequate funding (0.003,0.000,0.000,0.000) respectively were less than the p-value of 0.01. The negative coefficient (-0.355) of farm visit meant that lenders effort in visiting farmers would reduce default rate in AFC loans. However, the coefficient of disbursement timeliness, political lending and adequate funding had a positive coefficient implying a negative effect that increased AFC loans default rate. To mitigate default, credit officers need to disburse adequate loan expeditiously, refuse to yield to political interference and adhere to stipulations of lending policy. The study recommends that AFC should adequately and timely fund supervised borrower projects and own those projects by extending advisory and training inputs even as they embrace a neutral working environment that is free from political manipulation, compromise and influence.

Purpose – This study aims to analyze the influence of bank-specific and macroeconomic factors on credit risk in Islamic banking Methods – The research method used is panel data analysis using Islamic banking data in 10 countries with the largest Islamic financial performance from the first quarter of 2014 to the second quarter of 2023. This study uses three credit risk measurement indicators, namely NPF, COVNPF, and COVTOT. Findings – The results of the study show that bank-specific variables such as total assets, CAR, ROE, BOPO, and Zscore affect the credit risk of Islamic banks. Meanwhile, macroeconomic conditions show that high inflation rates reduce credit risk, but on the other hand, exchange rate depreciation will worsen bad debts in Islamic banks. Implications - The policy implications of this study are that the government needs to control macroeconomic variables and ensure banking health through appropriate and effective regulation and supervision. Originality – This study contributes to analyzing the determinants of credit risk in Islamic banks using three credit risk indicators Abstrak Tujuan – Penelitian ini bertujuan untuk menganalisis pengaruh variable internal bank dan variable makroekonomi terhadap risiko kredit di perbankan Islam Metode – Metode penelitian yang digunakan yaitu analisis data panel dengan menggunakan data perbankan Islam di 10 negara dengan kinerja keuangan Islam terbesar dari kuartal satu tahun 2014 hingga kuartal kedua tahun 2023. Penelitian ini menggunakan tiga indikator pengukuran risiko kredit yaitu NPF, COVNPF, COVTOT. Tumuan – Hasil penelitian menunjukkan bahwa variable internal bank seperti Total asset, CAR, ROE, BOPO dan Zscore mempengaruhi risiko kredit bank Islam. Sementara itu dari kondisi makroekonomi menunjukkan bahwa tingginya tingkat inflasi justru menurunkan risiko kredit namun disisi lain depresiasi nilai tukar akan memperburuk kredi macet di bank Islam. Implikasi – Implikasi kebijakan dari penelitian ini yaitu pemerintah perlu mengendalikan variable makroekonomi dan memastikan kesehatan perbankan melalui regulasi dan pengawasan tepat dan yang efektif. Orisinalitas – Penelitian ini berkontribusi dalam menganalisis determinan risiko kredit di bank Islam menggunakan tiga indikator risiko kredit.

Macro Stress Testing with a Macroeconomic Credit Risk Model for Finland

In this study, inspired by the Credit Portfolio View approach, we intend to develop an econometric credit risk model to estimate credit loss distributions of Turkish Banking System under baseline and stress macro scenarios, by substituting default rates with non-performing loan (NPL) ratios. Since customer number based historical default rates are not available for the whole Turkish banking system’s credit portfolio, we used NPL ratios as dependent variable instead of default rates, a common practice for many countries where historical default rates are not available. Although, there are many problems in using NPL ratios as default rates such as underestimating portfolio losses as a result of totally non-homogeneous total credit portfolios and transferring non-performing loans to asset management companies from banks’ balance sheets, our aim is to underline and limit some ignored problems using accounting based NPL ratios as default rates in macroeconomic credit risk modeling. Developed models confirm the strong statistical relationship between systematic component of credit risk and macroeconomic variables in Turkey. Stress test results also are compatible with the past experiences.

In 2018, corporate credit risk had notably increased in the Egyptian banking industry because its nonperforming loan (NPL) ratios had reversed the downward sloping curve shaping upward ones while the retail NPL ratio struggled the reverse, shaping a flat curve. In the present work, the paper aims to study the macroeconomic and microeconomic factors of corporate and retail credit risk to develop customized models that better predict the future value of the credit risk in the banks of Egypt to provide better insight to the credit officers and regulators to help in enhancing their credit decisions and regulations. The paper has utilized the Fixed-Random effects regression models for data analysis using panel data from 2013 to 2020. The findings showed that the macroeconomic and bank-specific variables affect the corporate and retail credit risks differently showing the importance of dividing the credit risk into categories to avoid generating misleading results and to create more robust and accurate predictive models of credit risk. The results also revealed that asset size, income diversification, loan-to-deposit ratio, interest rate, and external debt significantly affect corporate credit risk. In contrast, retail credit risk is affected only by macroeconomic variables such as external debt, GDP, interest rate, and foreign direct investment.

In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms’ forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME’s credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally.

Asymmetric effects of the business cycle on bank credit risk

This paper develops rare-event simulation methods for the estimation of portfolio credit risk—the risk of losses to a portfolio resulting from defaults of assets in the portfolio. Portfolio credit risk is measured through probabilities of large losses, which are typically due to defaults of many obligors (sources of credit risk) to which a portfolio is exposed. An essential element of a portfolio view of credit risk is a model of dependence between these sources of credit risk: large losses occur rarely and are most likely to result from systematic risk factors that affect multiple obligors. As a consequence, estimating portfolio credit risk poses a challenge both because of the rare-event property of large losses and the dependence between defaults. To address this problem, we develop an importance sampling technique within the widely used Gaussian copula model of dependence. We focus on difficulties arising in multifactor models—that is, models in which multiple factors may be common to multiple obligors, resulting in complex dependence between defaults. Our importance sampling procedure shifts the mean of the common factor to increase the frequency of large losses. In multifactor models, different combinations of factor outcomes and defaults can produce large losses, so our method combines multiple importance sampling distributions, each associated with a shift in the mean of common factors. We characterize “optimal” mean shifts. Finding these points is both a combinatorial problem and a convex optimization problem, so we address computational aspects of this step as well. We establish asymptotic optimality results for our method, showing that—unlike standard simulation—it remains efficient as the event of interest becomes rarer.

More attention to supply chain finance as a new form of financing channel is paid with the increasing highlight of the financing difficulty of small and medium-sized enterprises (SMEs). It is crucial for scientific and accurate risk assessment to control the control of the credit risk of supply chain finance. BP neural network model is used to open up a new way for credit risk assessment of SMEs based on the establishment and improvement of an index system of credit risk evaluation for SMEs. BP neural network model is a smart algorithm with self-learning and adaptive characteristics. The BP artificial neural network model was analyzed and detected in a practical case. The feasibility of the model was proved. The result is satisfactory. The model can be used to assess SMEs’ credit risk in the supply chain.

Relative default rates on corporate loans and bonds

Background: Agribusiness loans advanced by Agricultural Finance Corporation (AFC) in Mount Kenya Region have high default rate of 20.33% which compares unfavourably with 10% benchmark for all types of loans in Kenya. This is a challenge, given the strategic importance of agribusiness credit in mainstreaming livelihoods to alleviate poverty by offering occupational and professional opportunities. This study aimed at analysing effect of borrower’s socio-economic profile on AFC loan default rate in agricultural finance corporation, Mount Kenya Region.
 Methods: According to AFC records Mount Kenya region represents a branch network of 11 branches and a population of 3,002 agribusiness borrowers. Using a descriptive research design a sample of 300 borrowers was drawn from a combined list through systematic random sampling technique with an interval of ten. Primary data on borrower’s socio-economic profile was collected using a structured questionnaire. The data was analysed using Statistical Packages for Social Sciences (SPSS V.27) and Stata version. Using regression analysis, the effect of independent and dependent variable to predict default rate was estimated. ANOVA was performed to get the F-statistic so as to test for the adequacy of the regression model. The logit econometric model was used to specify the statistical relationship between the independent variable and AFC loan default.
 Results: Results of the study revealed that multiple borrowing and borrower-lender distance were significant at 5%. Farming experience, borrowing experience and off-farm income were significant at 10%, 5% and 1% levels of significance. Multiple borrowing and borrower-lender distance were found to have 5.5% and 0.8% associations with default rate, respectively.
 Conclusion: The findings show that to mitigate default, the borrower should avoid multiple borrowing and manage the friction of distance. The study is significant since it enlightens the credit stakeholders on collective efforts that are effective in addressing the problem of default. The study recommends government interventionist policy by facilitating uptake of agricultural insurance and subsidizing input costs. Borrowers are encouraged to embrace technology, team up as farming communities to look for markets and affordable inputs, adopt agricultural insurances and adhere to lending directives.

After the global economic crisis 2007–2011 and the recent post-low inflation 2014–2015, Viet Nam economies, its financial and stock market as well as real estate market experienced indirect and direct impacts on their operation, system and stock price. Although some economists have done researches on the relationship among macro economic factors such as: consumer price index (CPI), inflation, GDP…, this paper aims to consider the interaction between macro economic factors such as Viet Nam inflation and GDP growth rate, US inflation, exchange rate, risk free rate and other macro factors, and esp. their impacts on stock price of Vingroup (VIC), a big Vietnam real estate firm, in the context Viet Nam and the US economies receive impacts from global economic crisis. This is one main objective of this research paper. This research paper finds out VIC stock price has a negative correlation with risk free rate in VN and deposit rate of VN commercial banks, but has a positive correlation with lending rate in Vietnam. And the statistical analysis will generate results which help us to suggest macro policies in favor of the local stock and financial market. Real estate industrial risk over years has been affected much by macro economic risk, credit risk, and legal risk; therefore, government bodies need to issue proper macro economic legal, financial and credit policies in order to stimulate, develop stock market and reduce workload pressure for Vietnam bank system.