Abstract

     The aim of the work underpinning this paper has been to track the evolution of tail risk in banks’ NPL portfolios present under normal and worst conditions (before and during the pandemic of COVID-19), and to estimate the impact of sector concentration risk on amounts of economic capital. Results further allowed for analysis of different sectors with a view to determining which is riskiest. The study makes use of a multi-factor structural model, given that each sector is affected by a different systematic risk factor, with the assets of borrowers from the same sector thus correlated markedly, even as correlations between sectors are low. The research has in fact sought the further development of methodology proposed by Düllmann and Masschelein in 2006—in the direction of improved accuracy of economic-capital estimates, thanks to alternate means of mapping out the sectoral factor correlation matrix. The empirical analysis was based on individual data from Prudential Reporting under the National Bank of Poland, as well as market data. Results reveal an increase in tail risk through the 2015–2017 period, as followed by the onset of a decline. Where the paper’s second aim is concerned, there is found to be support for the idea that economic capital may be increased where sector concentration in the portfolio of a bank is accounted for. Tail risk is found to be concentrated in the sectors of construction and real estate, with accommodation and food services becoming more volatile during the pandemic. A channel for risk transfer between the financial and corporate sectors is thus found to exist. Thanks to the work done we have a better understanding of the impact of sectoral concentration of individual banks’ lending activities on level of risk, with the possibility of this gaining application as stress tests are conducted, and as supervisory recommendations from Poland’s Financial Supervision Authority are formulated.

Introduction

     Concentration risk is one of the specific types of risk in banking whose inappropriate management, non-subjection to appropriate policies and regulations, or incomplete measurement may all give banks financial problems. Examples here might be the concentration of bank lending in the energy sector in Texas and Oklahoma in the 1980s; as well as over-exposure to the construction and development sector in Sweden in the early 1990s, and in Spain and Ireland in 2000. The materialisation of concentration risk during the global financial crisis of 2008–2009 was in turn a source of huge losses for European and global banks that left them weakened economically, financially, and as regards operational security. Such circumstances ensure the huge importance from a macroprudential point of view of concentration risk being measured.

     The effects of the COVID-19 pandemic will hit home just as soon as we see restriction of the emergency measures that governments, central banks and regulators across Europe have introduced. That said, it is reasonable to expect that banks struggling currently with declining interest margins and low profitability, among other things, will prove to be most affected by the crisis, where there is marked concentration in sectors more affected by the pandemic, i.e. hospitality, transport and some manufacturing sub-sectors; as well as in those other sectors already characterised by a high level of non-performing loans on account of COVID. The pandemic may emerge as concentrating bank exposures to the domestic government sector to an excessive degree. This is to say nothing of Poland’s energy transformation, with its requirement for a very considerable amount of investment (given costs at the level of 1.6 trillion PLN estimated for 2021–2040.

Conclusion and policy implications

     This article examines the extent to which sector concentration contributes to growth in economic capital, as well as assessing the effectiveness of analytical methods in measuring sector concentration risk. Unexpected losses were estimated using the multivariate model described in the articles by Düllmann and Masschelein (2006), (2007), and Düllmann and Puzanova (2011), as derived indirectly from the model after Merton (1974). Additionally, this article has focused on the development of the methodology proposed by Düllmann and Masschelein, in the direction of improved accuracy of economic-capital estimation, thanks to alternate ways of mapping the sector factor correlation matrix. Moreover, the article contains a detailed description of the methodology applied.

     It is worth noting that the study used a unique database, inter alia with data concerning bank exposures to non-financial sector enterprises, corporate PDs and model LGD estimates (based on historical data). Portfolios were characterised by reference to their degree of diversification, with aggregate, relative exposures of domestic banking represented sector by sector. Exposures from the financial sector were not included, in recognition of that sector’s specific nature. In addition, it was possible to note the high degree of heterogeneity characterising sectors of industry, hence the study’s more-precise division into industrial groups.

Abstract

     Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

Introduction

     The development of small and medium-sized enterprises (SMEs) has attracted attention from scholars and practitioners over the globe. However, because of the tightening of credit criteria for corporate loans, SMEs are facing significant challenges, mainly including capital constraints, high operational costs, and ambiguous information (Yan & He, 2020). As a major component of the economy in China, SMEs contribute almost 90% of the number of enterprises, 80% of urban employment, 70% of GDP, 60% of technological innovation, and 50% of tax revenue (see https://www.ndrc.gov.cn/ for more detail). SMEs in China also face problems mainly including high financial distress, high financing costs, high operational risks, tightening financing channels, high fraud risks, and asymmetric financing information (Weng et al., 2016; Zhu et al., 2019).

     As a popular financing channel, supply chain finance (SCF) defined by Hofmann (2005) as the inter-firms optimization of financing and the integration of financing processes with customers, suppliers, and service providers to increase the value of all participating firms, has attracted attention from both practitioners and scholars alike. The Chinese government has developed some new financial policies to ease the financing pressure on SMEs, e.g., Promoting SME Development Plan (2016–2020), which seek to “promote more supply chains to join the financing service platform of SMEs”. Similar initiatives are underway in other countries and regions, such as the United States, United Kingdom, Japan, Canada, South Korea, Europe, and Mexico. SCF is also being used to promote the development of SMEs. For example, the Office of the United States Trade Representative (USTR) is implementing a series of initiatives to address the financing problems of SMEs, including SCF

Conclusions

     To our best knowledge, this is the first study to consider the value of multi-source information fusion to predict the SMEs’ credit risk in SCF, and presents some managerial implications. We predict the credit risk of SMEs in SCF with an imbalance sampling strategy on machine learning techniques. Considering the value of multi-source information fusion in the big data era, we construct a broader knowledge base, including financial information, operation information, innovation information, and negative events, to predict the credit risk of SMEs in China, and develop new models to simultaneously solve for key predictor selection and imbalance classes. We then adopt six evaluation criteria to compare the prediction performances of the six machine learning techniques—SVM, NN, DT, RF, bagging, and GB—based on the data of VA and VS, respectively. We compare the results of new models via a re-sampling strategy for baseline models on VA and VS; the results indicate that the proposed CSL-RF model is optimal in terms of accuracy and robustness. The empirical results indicate that the financial-based information is the main source to predict SEMs’ credit risk in SCF, and the multi-source information fusion is meaningful. In addition, based on the preferred CSL-RF model, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. Finally, we generate strategic insights for market participants, such as regulators, investors, and managers.

Abstract

     The evaluation of associated credit risk in supply chain is a difficult problem in the current credit risk management practice. Based on graph theory and fuzzy preference theory, we put forward a new evaluation method of associated credit risk in the supply chain. First, the credit risk of enterprises in supply chain are divided into “self-own credit risk” and “credit risk contagion”. Second, the indicator system of self-own credit risk evaluation of enterprises in the supply chain is designed, and the fuzzy comparative judgment matrix of self-own credit risk evaluation indicator is obtained by using fuzzy preference relation, and the evaluation basic model of “self-own credit risk” is established. On the basis, the evaluation derivative model of “credit risk contagion” is established. Furthermore, by integrating the two kinds of evaluation results, the credit risk of enterprises in supply chain is evaluated comprehensively, and the contagion effect of associated credit risk in supply chain based on trade credit risk contagion (TCRC) is revealed.

Introduction

     With the intensification of economic globalization, the supply chain gradually extends to the depth, showing the characteristics of cross-industry, cross-region, multi-member and multi-correlation. At present, the supply chain has evolved into a complex supply chain system composed of a number of interassociated enterprises[1]. The correlation relationship in the supply chain leads to the contagion effect among the credit risks of enterprises, and forms the contagion network of the associated credit risk in the supply chain[2]. The associated credit risk in the supply chain presents a strong contagion effect, which directly endangers the commercial banks that provide loans to the enterprises in the supply chain[3]. Therefore, when commercial banks evaluate the credit risk of enterprises in the supply chain, they must focus on the evaluation of the contagion effect of associated credit risk in the supply chain. Under the framework of traditional credit risk evaluation of commercial banks, it is generally believed that the credit status of enterprises changes independently. Therefore, when traditional credit risk assessment methods are used to evaluate enterprises in the supply chain, they are often isolated from the supply chain environment and only evaluate the credit status of a single enterprise without involving the interaction of credit risk among enterprises in the supply chain, resulting in inaccurate evaluation results. It is difficult to provide scientific theoretical basis for the credit decision and risk control of commercial banks.

Conclusions

     Traditional credit risk evaluation methods of commercial banks fail to consider the contagion of credit risks among associated enterprises, leading to unscientific credit risk evaluation results of loan applying enterprises, unreasonable credit decision-making and risk control measures, and thus the credit risks faced by commercial banks themselves are increased accordingly. This paper divides the credit risks of enterprises in the supply chain into “self-own credit risk” and “credit risk contagion”. Based on the risk contagion network formed by trade credit, this paper constructs the evaluation basic model of “self-own credit risk” and the evaluation derivative model of “credit risk contagion” in the supply chain, and combines the two evaluation models to comprehensively evaluate the credit risk of enterprises in the risk contagion network of the supply chain. The evaluation method proposed in this paper not only effectively evaluates the credit risk of the enterprises in the supply chain risk contagion network, reveals the contagion effect of the associated credit risk in the supply chain, but also helps the commercial banks reduce or even eliminate the negative impact of the associated credit risk in the supply chain, and reduces the potential loss of the commercial banks.

Abstract

     Using daily credit default swap (CDS) data, we find a positive relation between corporate credit risk and unexpected monetary policy shocks during FOMC announcement days. Positive shocks to interest rates increase the expected loss component of CDS spreads as well as a risk premium component. However, not all firms respond in the same manner. We show that firm-level credit risk is an important driver of the monetary policy response, both in credit and equity markets, and its role is not diminished by the inclusion of other risk proxies. A stylized corporate model of monetary policy, investment, and financing rationalizes our findings.

Introduction

     Understanding the transmission of interest rate shocks to corporations is of paramount importance to policymakers and economic researchers, as corporate borrowing is widely used to fund investment, production, labor, and other real activities. While economic theory suggests that firms obtain credit at a premium that compensates for default risk, it is still widely debated as to how corporate credit risk is affected by monetary policy. This transmission mechanism has played a crucial role in policy responses over the last two decades.

     In this paper, we connect unexpected interest rate movements to fluctuations in credit risk to shed more light on this transmission mechanism. To measure credit risk at the firm-level, we take advantage of daily quoted prices on the corporate, single name, credit default swap (CDS) market. Using CDS rates allows us to conveniently separate the two main components of credit spreads, an expected loss component and a credit risk premium, and independently study their reaction to monetary policy surprises.2

Conclusion

     Monetary policy surprises significantly affect firm-level credit risk. Consistent with recent evidence, we document that both components of firm-level credit risk (i.e., the expected loss and the risk premium components) and equity prices respond more to monetary policy surprises when firms have higher credit risk, as measured by their CDS spreads. Additionally, the importance of firm-level credit risk for the propagation of monetary policy shocks does not diminish when we contemporaneously control for book leverage and market size, two characteristics commonly associated with firm-level risk.

     To rationalize the heterogenous response of credit risk and the noisiness of leverage as a risk indicator, we construct a stylized three-period model that features endogenous financing and investment decisions together with a monetary authority that sets short term interest rates. Central to our results is an intermediary pricing kernel for which larger prices of risk related to interest rate shocks correspond to larger responses of credit spreads and real quantities. We show in the model economy that firms with higher ex-ante spreads, that are closer to default, are those that are more sensitive to interest rate shocks. Meanwhile, firms with higher leverage are able to afford such debt capacity due to their fundamental strength and, as a result, their credit spreads are less sensitive to monetary policy shocks.

     Overall, our work suggests that a simple and intuitive measure such as leverage might not be as effective as a more direct measure of credit risk such as CDS spreads, to understand the pass through of monetary policy at the firm level.

Abstract

     In today’s Chinese economic system, market system, and development goals, MSMEs play an essential role. The availability of capital is a necessary condition for the development of MSMEs. However, in China, MSMEs still cannot effectively eliminate the development dilemma that financing is difficult and expensive. The deep-seated reason lies in the imperfect credit evaluation system of the banking industry for MSMEs. The purpose of this paper is to establish a mathematical model for credit risk evaluation of MSMEs and to establish a new bank credit evaluation strategy to support and back up MSMEs of different sizes accurately.

Introduction

     As economic globalization develops, Chinese MSMEs have seen rapid development. However, with the further development of MSMEs, the shortage of capital often becomes a significant factor limiting the further development of MSMEs. Due to the limitation of the size of MSMEs, their risk tolerance is weak, leading to further increase of enterprise credit risk and the difficulty of credit assessment by commercial banks. The purpose of this paper is to establish a mathematical model for assessing the credit risk of MSMEs and to establish new bank credit strategies to support and back up MSMEs of different sizes accurately. In this paper, an evaluation model is established by combining the machine learning model XGBoost algorithm with AHP, and the combined model is used to construct and analyze the model to quantify further and evaluate the credit risks of MSMEs. While ensuring the interests of commercial banks, more MSMEs are provided with loans to increase the amount of loanable capital of the whole society. Promote the further development of MSMEs and promote social progress.

Model Evaluation and Conclusions

     This paper establishes a reasonable mathematical model and formulates the bank’s credit strategy by evaluating and ranking the credit risk. We first use the analytic hierarchy process to determine the factors affecting the credit risk and then use the comparative method to quantify the importance of each factor. After the consistency test is passed, an acceptable pairwise comparison matrix is constructed. MATLAB software calculates the proportion of N factors in this layer in target Z. The proportion in the criterion layer is written into a vector and normalized to obtain the weight vector.

     Then, this paper uses SPSS software to quantify the stability of supply and demand in the criterion layer and then quantify the relationship between customer churn rate and reputation level given in the data to calculate the average value of the three levels of ABC. After data preparation, this paper classifies the eigenvalues for similar machine learning. We use Mpai software to substitute effective indicators into the machine learning model XGBOOST regression for training. In this paper, xgboost machine learning is used to substitute the normalized weight vector in the analytic hierarchy process to obtain the quantitative regression value of the company’s strength. Finally, the credit risk rating evaluation formula is constructed using the criteria layer weight and the lower level quantitative data. The final enterprise credit risk ranking is obtained using Mpai software, and the credit strategy is given scientifically and reasonably according to the ranking.

Abstract

     Because cash flow is a critical issue for companies, it is important to effectively operate cash flow to mitigate liquidity risks. However, compared with research on the bullwhip effect, few studies have analyzed the effects and causes of the cash-flow bullwhip in the supply chain. None has considered the influence of credit risk on the cash-flow bullwhip effect from downstream to upstream throughout the supply chain. Thus, this study develops a mathematical model to investigate the influence of credit risk on the cash-flow bullwhip. To achieve this, it analyzes the variability of each member’s account receivable, account payable, and cash level along with three financial performance measures: account receivable turnover, account payable turnover, and cash conversion cycle. The excessive inventory level created by the bullwhip effect is known to cause the cash-bullwhip effect, which leads to supply chain members experiencing liquidity problems. However, the results of this study demonstrate that a consideration of credit risk increases the amounts of account receivable, account payable, and cash from downstream members to upstream members. In addition, this study demonstrates that when considering the credit risk, the account receivable turnover index accurately illustrates the cash-bullwhip effect of each member throughout the supply chain.

Introduction

     Supply chain management controls material, information, and cash flows to efficiently convert raw materials from upstream into products to downstream through the supply chain. Because supply chain management is considered essential for companies wishing to achieve a sustainable business operation, the management of material, information, and cash is deemed important in the contemporary business environment. To improve the efficiency of the supply chain, most companies have strived to identify and solve the problems occurring from the flows of material, information, and cash through the entire supply chain. However, compared with the efforts companies expend on the management of material and information flows, there has been little focus on the cash flow of the supply chain as a means of improving efficiency.

     One of the critical problems in the supply chain is a bullwhip effect, which refers to an increasing inventory fluctuation moving from downstream members to upstream members in response to distorted information on customer demand. Distorted demand results in an inefficient production and excess inventory which exacerbates the liquidity problems of supply chain members. In other words, the bullwhip effect further leads to a cash-bullwhip effect, which is defined as an increase in the variance of a cash conversion cycle from downstream to upstream through the supply chain that corresponds to inventory fluctuation (Tangsucheeva & Prabhu, 2013). Like the bullwhip effect, the cash-bullwhip effect is important for companies wishing to achieve sustainable business operations through the efficient and effective management of production and inventory. To mitigate the effects of the cash-bullwhip effect, a prerequisite is to identify its causes and impacts on all members of the supply chain.

Conclusion

     Although the bullwhip effect and the credit risk are critical issues in material flow and cash flow throughout the supply chain, no studies have considered these two important issues simultaneously. For these reasons, this study investigated the impact of credit risk on the cash-flow bullwhip effect from downstream members to upstream members along the supply chain in terms of account receivable, account payable, and cash. To analyze the cash-bullwhip effect, three financial performance indicators were used— account receivable turnover, account payable turnover, and cash conversion cycle.

     Having applied the developed cash-bullwhip effect mathematical model, the results demonstrate that credit risk consideration improves the liquidity problems of each supply chain member by increasing the amounts of account receivable, account payable, and cash from downstream to upstream. The results also indicate that the account receivable turnover accurately indicates the cash-bullwhip effect of each member. However, the account payable turnover and the cash conversion cycle do not accurately illustrate the cash-bullwhip effect phenomenon in the case of credit risk consideration.

Abstract

     This paper explores the determinants of credit risk for the Colombian economy, a small emerging economy in Latin American. Using a sample of 28 large banks over the 2009–2019 period and the dynamic data panel approach, we find that the macroeconomic environment’s deterioration affects the credit risk perception held by banks as measured through non-performing loans and loan loss provisions. On the other hand, a better political environment brought about by peace accords smoothed such an impact. Estimates indicate different reactions when distinguishing by loan type. Business credit depends heavily on unemployment, while consumer credit risk is more sensitive to the interest rate. In the case of mortgage loans, economic growth and the unemployment rate are the most critical variables to mitigate risk. These results shed light on the impact of the economic environment on credit lines with different features.

Introduction

     The current economic recession caused by the COVID-19 pandemic has drawn attention to the consequences it may have on the banking system. As a result, there is great interest in knowing which indicators can provide efficient signals about financial instability (Chau, Lin, and Lin 2020). Loan performance is an essential channel to analyze the effect that macroeconomic factors have on banking crises. Unfavorable economic conditions such as lower economic growth and high unemployment can increase loan defaults and bankruptcies, so the issue of the procyclicality of the financial system should attract the attention of both academics and policymakers because it can exacerbate the economic downturn (Castro 2013: Cecchetti and Kohler 2014; De Moraes and De Mendonça 2019).

     Credit risk is the main channel for relating economic shocks to bank balance sheets. It is defined as the risk that a loan will not be paid (partially or totally) to the lender and it is a problem that has received extensive theoretical research. The seminal paper of Altman and Saunders (1998) initially surveyed the approaches that existed on the subject. Credit risk analyses have changed from so-called banking expert systems analyses to more objective schemes. Until the late 1980s, bankers used microeconomic information on the borrower such as reputation, capital, ability to pay, and collaterals to make subjective judgments about credit risk. By the late 1990s, these models began to fail because they did not consider the economic environment in which banks operate. New approaches began to explore alternatives to explain credit risk based on the possible states of the economy. In particular, the study by Salas and Saurina (2002) pioneered this issue by combining micro and macroeconomic data to assess credit risk.

Conclusions

     The global economic crisis is paying attention to the problems it can bring to the financial system. In this sense, this study analyzed the effect of macroeconomic perspectives on credit risk for the Colombian case. Through a dynamic data panel approach for the period 2009–2019, it was found that the macroeconomic environment is the primary determinant of Colombia’s credit risk. Credit risk increases with declines in GDP growth, rising unemployment, and tightening monetary policy rates.

     In 2020 the Colombian economy entered a severe recession and, according to the results of this study, credit risk is likely to increase. In terms of economic policy, this means a threat to financial stability and the possibility of a credit crunch (Louzis, Vouldis, and Metaxas 2012), so different incentives for each type of loan are needed to recover the financial system. Government endorsements and mortgage subsidy policies can help mitigate financial market failures.

Corporate governance is an important determinant of corporate performance. Poor corporate governance can damage the interests of shareholders, and may lead to business collapse. This paper expands the literature on credit risk management by assessing the effectiveness of aspects of corporate governance for predicting financial distress in a dynamic discrete-time survival analysis model. It is a comprehensive, up-to-date and thorough study, which uses a large range of corporate governance measures, financial ratios and macroeconomic variables in a panel data structure over a 17-year period. Furthermore, the paper addresses the relationship between government ownership and the risk of financial distress in China. The results suggest that although corporate governance alone is not sufficient to accurately predict financial distress, it can add to the predictive power of financial ratios and macroeconomic factors. In addition, the model provides insights into the role of state ownership, independent directors, institutional investors and some personal characteristics of the Chair of the board. Implications are made regarding them and the debt and bankruptcy problem in China and Asia.

Introduction

Predicting corporate bankruptcy or financial distress has been a vibrant topic in banking, business and finance because of its importance to creditors such as banks. For corporate debtors, management quality is a key factor in their performance. A firm’s bankruptcy or financial performance will affect investments and debt repayments, and therefore needs to be accurately predicted. It is not surprising that this topic has received a lot of attention in academic and practical work. Risk-taking decisions of creditors will depend on their ability to analyze or predict the risk involved. There is a vast body of literature on bankruptcy prediction models that can be classified into accounting based models using financial ratios (e.g. Altman (1968) and Bonfim (2009)) and market based models using share prices (e.g. Milne (2014) and Campbell et al. (2008)) respectively. Corporate governance measures are less common in bankruptcy prediction literature, as they do not represent hard information such as financial ratios, but rather soft information, although behaviors such as default on debt, financial distress and bankruptcy have been found to be linked to corporate governance (see e.g. Daily et al. (2003)). The research which aims to understand the role of corporate governance and subsequent company performance is summarized in the next section of this paper.

Credit risk is an important issue in the decision-making and profit of the banking industry. Credit risk is still a single biggest risk that is difficult to offset for banks and it expresses the concept of future loss. Because the customer does not fulfil the repayment obligation, the credit risk also embodies the loss of the bank’s profit. Usually, the general approach of credit risk assessment is to apply the classification model to past customer data, including default and non-default customers, so as to find the relationship between user characteristics and potential default. The credit risk assessment model based on statistical data has become the main analysis tool for the financial institutions to assess the credit risk. By analysing the multiple risk factors of the evaluation object, the credit risk assessment is an independent process of assessing the borrower’s willingness and ability to repay. The credit risk assessment model has been widely used to assess corporate risk by bond investors, debt issuers, and government officials. They provide a means to determine the risk premium and bond market, so that companies can assess the possible return on investment to issue bonds. The advantages of building a credible credit risk assessment system are: reducing the cost of credit analysis, ensuring fast decision-making, guaranteeing credit collection and reducing possible risks. The credit risk assessment was initially judged by the personal experience manager, and then based on the 5C factor. However, with the rapid increase of applicants, it is almost impossible to do the work manually. Many institutions in the credit industry are proposing new models to support credit decisions. Recent studies have shown that the existing artificial intelligence (AI) technology, such as decision tree (DT), support vector machine (SVM) and so on, in the problem of credit risk assessment, shows a better performance than the statistical model and optimization method. Different from statistical models, AI model does not require the assumption of variable distribution, and can acquire knowledge directly from training data sets. In the field of credit risk assessment, especially when the credit risk assessment problem is nonlinear mode classification, the performance of AI model is better than that of the statistical model.

Studies on corporate risk management and derivatives use commonly view derivatives as hedging instruments (e.g., Smith and Stulz, 1985; Rogers, 2002; Aretz and Bartram, 2010). The role of credit derivatives as credit risk management instruments in banks is widely acknowledged (e.g., Hirtle, 2009; Nijskens and Wagner, 2011). Banks have dominated the credit derivatives market, in which CDS are the most commonly used credit derivatives globally (British Bankers’ Association (BBA), 2006; Minton et al., 2009; International Swaps and Derivatives Association (ISDA), 2010). However, the incentives underlying the use of CDS and the effectiveness of such credit risk management remain unknown (Minton et al., 2009). Our study contributes to filling this gap. CDS allow the transfer of credit risk across firms, whereby buying CDS allows a bank to short exposure to credit risk at a known cost (i.e., insurance premium). From the buyer’s perspective, the economic effect of a CDS contract is similar to that of an insurance contract, because it protects the buyer against the default of the underlying asset during the life of the contract (Duffee and Zhou, 2001). This protection is important for banks that are constantly exposed to the credit or default risks of risky assets (e.g., loans and mortgages). We can expect that banks with hedging motives or those managed by risk-averse managers are more likely to buy CDS that transfer the credit risks of risky assets to the sellers of such derivatives. However, bank managers are not always risk-averse, because their risk-taking appetites would enhance when stock options are included in their pay packages. Studies on executive compensation suggest that equity-based pay, such as stock options, had induced risk-taking behaviours among bank managers in the build-up of the 2007-2008 crisis (e.g., Bebchuk et al., 2010; Fortin et al., 2010; Minhat and Abdullah, 2016). Some studies also suggest that stock options can discourage managers from hedging risk (e.g., Smith and Stulz, 1985; Tufano, 1996; Rajgopal and Shevlin, 2002; Aretz and Bartram, 2010; Bakke et al., 2016). This is because stock options contain an important feature that can induce, rather than restrain, managers’ risk-taking appetites. This feature is described as vega, which measures the extent of risk-taking incentives induced by stock options (Guay, 1999; Rogers, 2002; Coles et al., 2006; Minhat and Abdullah, 2016). Vega is the measurement of a stock option’s sensitivity to the volatility (or risk) of the underlying stock. Because the value of a stock option is positively related to the volatility of the underlying stock, the presence of vega will discourage managers from hedging against risk or volatility (e.g., Rajgopal and Shevlin, 2002; Aretz and Bartram, 2010). If hedging against credit risk can reduce a bank’s stock volatility (Nijskens and Wagner, 2011), then that risk management strategy is not in the best interest of managers who wish to increase the value of their stock options. Therefore, we conjecture that vega will provide bank managers with little incentive to buy CDS for hedging purposes.

The financial and economic crisis, reaching most countries of the world, significantly affected the economy of Ukraine, in particular the state of the economic security in its banking sector. Since, as we know, it is banking system of the country that concentrates large cash resources and ensures their movement, distribution, and reallocation, in the interests of economy In the context of banking sector disfunction there appear a deterioration in economic relations among business entities; it becomes a threat to the economic security of the country. Currently, the most important aim of national economy long-term development is the creation of high-quality and efficient financial system, which would be competitive in the global market. That is gaining particular importance in the context of European integration. With that, a significant role is given to the banking system as a source of economic development. Its further functioning should be mainly aimed at growth rate, which outpace other financial institutions. As is known, a modern bank is an element of a credit system. It attracts free funds that are withdrawn from the economic process and then granted to contractors for the temporary use. All banks activities involve risks. In our opinion, minimizing them is a primary objective of bank risk management. Therefore, to provide banking activities successfully banks need to establish and improve the risk management system. Only in this way a bank will be able to identify, assess, minimize and control a risk of a certain type. All banking risks are interrelated. A credit risk is a risk of losing funds provided for temporary use in case of borrower’s failure to return them on time and pay interest for the use of credit resources. Accordingly, the credit risk is one of the most tangible in financial banking.

The recent financial crisis started in the sub-prime mortgage sector in the United States in the fall of 2008 to propagate and trigger a financial crash that shook the international stock markets, and led to the collapse of the pillars of the global finance and then to a recession that aوٴected the entire planet. Нis crisis caused further damage and casualties throughout the world to the extent that it became one of the major financial crises in history, which raised the curiosity of many analysts and economists. It is in fact an opportunity to review the current foundations of the international financial system characterized by an excess of financialization which distinguishes the real from the financial economy and encourages indebtedness and speculative driіs, which weakens the system and exposes it to more crises in the future. In Europe, an amount of 275 billion Euros at the marginal rate of 4.08% was injected in August 2007 by the European Central Bank (ECB) in order to help banks avoid successive liquidity problems that investors were subject to and also to avoid the paralysis of the credit market. Нe amount of loans given by banks was remarkably reduced. Нis step was reached aіer a phase characterized by the increase of the degree of the loans oوٴered along with the increase of the number of default debtors. Because of the subprime crisis, the eighth British bank, Northern Rock, a specialist in mortgages, fell into a situation of illiquidity. For this reason, the bank of England intervened to help it by playing the role of the lender of last resort (RDP). Нe main source of the lack of liquidity within this institution is the mistrust of the second-tier banks that refused to lend liquidity, fear of its possible exposure to the subprime mortgage market. Нis “Subprime Credit” emphasized Islamic finance and presented it as a means of regulating the world economy, especially aіer the many weaknesses that finance has experienced. In this fragile macroeconomic context, the sentiment of the financial markets fluctuated according to the actions of the authorities.

 The assessment of financial credit risk is emerging as an important research topic in the banking industry. The financial credit risk indicates the risk associated with financing, in other words, a borrower cannot pay the lenders, or goes into loan default. Credit risk assessment has become a particularly challenging issue for banks and financial institutions to access the performance of borrowers (customers), serving as the impetus to evaluate the credit admission or potential business failure of customers in order to make early actions. The great loss resulted from the financial distress or bankruptcy of customers usually leads to considerable criticism on the functionality of financial institutions due to the inappropriate evaluation of credit risk. Most governments are forced to implement rescue plans for the banking systems with more effective credit risk assessment. In China, the massive credit boom poses challenge for the quality of bank assets. In fact, total bad loans reached 1.27 trillion yuan at the end of 2015, the highest since the global financial crisis, on the back of an economic slowdown and a ballooning corporate debt. An meticulous management information system is in urgent requirement. Credit risk assessment, which enables or supports an early-warning detection and fast response mechanism, is a key in this system. Since 2004, the China Banking Regulatory Commission (CBRC), which is responsible for regulation of banking industry in China, enables a reporting system for credit data collection. In recent years, CBRC has attached much importance to risk characteristics mining, custom’s behavior analysis and risk assessment model.