Sources of Credit Risk

Abstract. The objective of this article is theoretical and methodological justifying of determining algorithm of the cost of debt capital for enterprises functioning in emerging markets (EM). The methods of research: analysis and synthesis, system analysis, comparative analysis, empirical and statistical methods, factor analysis. Results. In this article key determinants of interest rates on debt capital for enterprises and their impact on the procedure of discount rate calculation are determined. The issue of the cost of debt calculation of enterprises in condition of absence of complete information concerning systematic and non-systematic crediting risks is studied. Differences between interest rate on the loan fixed in credit agreement and expected by creditors the cost of debt are identified. It is determined that the key factor impacting the deviation level of market value of debt capital from the nominal, and respectively, deviation of the cost of debt from the cost of capital is probability of default (PD). At the minimum values of PD, the contract interest rate corresponds to the rate of cost of debt and it is advisable to use it for discount rate calculation. Critical analysis of alternative methodological approaches of the cost of debt calculation is made. Ways of integrating of market information concerning credit default swaps into the process of expected cost of debt calculation are justified. Factors of shadowing of rates of the cost of debt and ways of reducing of shadow transactions’ level in the credit market are identified. Conclusions. At high PD values, expected by market premium for default risk may exceed the contract interest rate, which necessitates constant monitoring of credit risks and appropriate adaptation of interest rates. In the paper the algorithm of such adaptation are proposed. It is shown that in the case of non-use of interest rates adjustment taking into account changes in PD, CDS and LGD, premium for creditors’ systematic risk can differ significantly from market values of similar enterprises (peer-group), and estimated value of the cost of debt can acquire negative values. Contract (promised) interest rate should be set in such way that the premium for systematic risk of providing debt capital will be at the level of similar companies and does not change significantly as a result of probability of default changes. If in practice the opposite situation occurs, it is the evidence of contract interest rate shadowing, absence of effective system of assessment and management of credit risks. For solving the problem of interest rate transparency and filling of information gaps concerning PD borrowers in EM countries, should intensify CDS market. Keywords: debt capital, default probability, non-performing loans, credit default swap, credit spread, debt capital premium, shadow economy. JEL Classification E47 Formulas: 16; fig.: 0; tabl.: 3; bibl.: 15.

—Credit risk management is an essential aspect of financial management for lenders and borrowers alike. This paper provides an overview of credit risk management, including its measurement and mitigation. The measurement of credit risk involves the use of proprietary risk rating tools and requires qualitative and quantitative techniques to rate the risk of business borrowers. Credit risk can be mitigated through credit structuring techniques, sensitivity analysis, and portfolio-level controls. Basel I, Basel II, and Basel III are rules made by the Basel Committee on Banking Supervision to ensure banks have enough money to cover any losses they might have. The traditional 5C model, the FICO scoring, Vantage Score, decision trees, logistic regression, and neural networks are among the many of the credit scoring models addressed in the paper. Credit scores are calculated and risks are monitored using statistical models, credit scoring software, risk assessment tools, data visualization tools, and credit bureau reports. The combination of these analysis tools helps lenders and financial institutions identify patterns and trends, assess borrower creditworthiness, and mitigate credit risk. This paper highlights the importance of effective credit risk management in ensuring the  nancial stability of lenders and borrowers.

Measuring Abnormal Credit Default Swap Spreads

The introduction of credit default swaps（CDS）in China plays an active role in the alleviation of liquidity shortages in bond market due to the break of the rigid payment. However, under current information disclosure rules in China, the inherent risks of products and counterparty risks cannot be accurately identified and be prevented in the operation of credit default swaps. Furthermore, with the management of credit risk, it also brings new risks or even triggers systematic financial risk. Therefore we must lock risks in a controllable range by establishing supporting systems, and the improvement of information disclosure system which is fitting the features of credit default swaps is the key. Credit default swaps are taken as derivatives of the management of credit risk, so the information that participants need to know most when they are trading must be closely related with credit risk, including credit risk information of reference entities and counterparty credit risk information. Therefore, it is necessary to take the identification and prevention of credit risk as the objective, and construct perfect information disclosure rules centering on risk relevance”, to guarantee the standard operation of credit default swaps in China.

The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.

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.

The intersection of market and credit risk

This paper examines the latest interest rate trends and credit risk management for the performance of Pakistan’s banking industry and drawing lessons from the default of Silicon Valley Bank. In the ever-evolving landscape of the banking industry, the delicate balance between interest rates and credit risk management emerges as a pivotal force shaping the sector's performance. This research provides a roadmap for financial institutions to harness the moderating influence of interest rates on credit risk, ultimately enhancing performance and resilience. Pakistan's banking industry must learn from the experiences and apply them to their own risk management practices. The study emphasizes the need for enhanced risk assessment frameworks, including thorough borrower evaluation, stress testing, and scenario analysis. It was conducted by well-structured questionnaire from a sample of 230 seasoned, experienced employees of top ten banks of Pakistan. The results of multiple regression showed that three variables: Credit Risk Management (CRM hereafter), Bank Performance and Interest rate are interconnected, statistically significant, affect each other and interest rate contribute in performance and strengthen CRM techniques. The study is quite useful for understanding and comprehending the changes in Pakistan’s banking industry over the past and in the latest scenarios of higher interest rates. Future contribution will be that this research will supply the foundation for other researchers who wish to dig into monetary policies and related disciplines.

Multi-country risk management of longevity risk provides new opportunities to hedge mortality and interest rate risks in guaranteed lifetime income streams. This requires consideration of both interest rate and mortality risks in multiple countries. For this purpose, we develop value-based longevity indexes for multiple cohorts in two different countries that take into account the major sources of risks impacting life insurance portfolios, mortality and interest rates. To construct the indexes we propose a cohort-based affine model for multi-country mortality and use an arbitrage-free multi-country Nelson–Siegel model for the dynamics of interest rates. Index-based longevity hedging strategies have the advantages of efficiency, liquidity and lower cost but introduce basis risk. Graphical risk metrics are a way to effectively capture the relationship between an insurer's portfolio and hedging strategies. We illustrate the effectiveness of using a value-based index for longevity risk management between two countries using graphical basis risk metrics. To show the impact of both interest rate and mortality risk we use Australia and the UK as domestic and foreign countries, and, to show the impact of mortality only, we use the male populations of the Netherlands and France with common interest rates and basis risk arising only from differences in mortality risks.

The real estate sector plays a pivotal role in driving economic growth. However, it faces significant challenges related to credit risk and sustainable supply chain practices. The management of credit risk and the integration of Environmental, Social, and Governance (ESG) factors into credit risk assessment have garnered increasing attention. This paper investigates the influence of sustainability practices within the real estate supply chain on credit risk management, focusing on the case study of China Evergrande Group. Through an analysis of Evergrande Group's financial reports and relevant policy changes via literature review and case study methodology, this research delves into the underlying causes of its credit risk as well as its repercussions on the supply chain. The findings indicate that Evergrande Group's high leverage coupled with an aggressive debt expansion strategy has resulted in a heightened accumulation of credit risk amid evolving policies and market conditions. Consequently, it is recommended that real estate enterprises enhance their mechanisms for assessing supply chain-related credit risks, establish early warning systems for potential credit issues, promote information sharing among stakeholders, and refine their contractual agreements and guarantee frameworks to mitigate credit risks while promoting sustainable development within their supply chains

Credit Risk Mitigation Based on Jarrow-Turnbull Model

Financial models are an inescapable feature of modern financial markets. Yet it was over reliance on these models and the failure to test them properly that is now widely recognized as one of the main causes of the financial crisis of 2007–2011. Since this crisis, there has been an increase in the amount of scrutiny and testing applied to such models, and validation has become an essential part of model risk management at financial institutions. The book covers all of the major risk areas that a financial institution is exposed to and uses models for, including market risk, interest rate risk, retail credit risk, wholesale credit risk, compliance risk, and investment management. The book discusses current practices and pitfalls that model risk users need to be aware of and identifies areas where validation can be advanced in the future. This provides the first unified framework for validating risk management models.

Insurance companies are facing major challenges that point to the need for control process and risk management. Risk management in insurance has a direct impact on solvency, economic security, and overall financial stability of insurance companies. It is very important for insurance companies to adequately calculate risks to which they are exposed. Asset liability management (ALM), as an integrated approach to financial management, requires simultaneous decision-making about categories and values of assets and liabilities in order to establish the optimum volume and the ratio of assets and liabilities, with the understanding of complexity of the financial market in which financial institutions operate. ALM focuses on a significant number of risks, whereby the emphasis in this paper will be on interest rate risk which indicates potential losses that may reflect in a lower interest margin, a lower value of assets or both, in terms of changes in interest rates. In the above context, the aim of this paper is to show how to protect from interest rate changes and how these changes influence the insurance market in Montenegro, both from the theoretical and the practical point of view. The authors consider this to be an interesting and very important topic, especially because the life insurance market in Montenegro is underdeveloped and subject to fluctuations. Also, taking into account the fact that Montenegro is a country that has been making serious efforts to join the EU, it is expected that insurance companies in Montenegro will strengthen their financial position in the market even using the ALM traditional techniques, which is shown in this paper.

<p>Credit risk refers to the probability of financial loss resulting from a borrower’s failure to repay a loan. Essentially, it encompasses the risk that a lender may not receive the owed principal and interest, leading to disrupted cash flows and increased collection costs. Lenders can mitigate credit risk by analyzing factors related to a borrower’s creditworthiness, such as their current debt load and income. In the last decade, many banks have started to make use of models to assess the risks of lending credit. The credit risk models are very complex and include algorithm-based methods of assessing credit risk. Such a model aims to help banks quantify, aggregate, and manage credit risk. Despite the method, the focus of credit risk assessment stays on credit quality and risk exposure. Strategies to reduce losses and manage risks are pertinent in credit risk management. However, banks have to organize and manage the lending function professionally and proactively and use advanced techniques to measure and manage risks. Credit risk management has become a hot topic due to the ongoing global economic crises, the rapid digital transformation, the recent technological innovations, and the growing use of artificial intelligence in banking. Regulators expect banks to have a clear and comprehensive understanding of their customers and their credit risk and to be transparent and capable in this area. As the Basel regulations change, banks will face more regulatory pressure. To meet the changing regulatory demands and to manage risk better, many banks are changing their credit risk practices. However, banks that see this as only a compliance issue are missing the point. The research findings indicate that the bank’s credit management practices have strongly influenced its profitability. In brief, manoeuvring through the complex risk terrain poses a significant hurdle for financial organizations. This requires ongoing adjustments and strategic choices to ensure both stability and profitability. Consequently, persistent academic research is essential to guide management, governments, and regulators.</p><p><strong> </strong></p><p><strong>JEL</strong>: G21, G32, G33, C53, E58, D81</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0771/a.php" alt="Hit counter" /></p>

PurposeThe credit crunch of 2008 and recent COVID-19 influences underscored the importance of liquidity and credit risk management in businesses and financial institutions. The purpose of this study is to investigate the impact of liquidity risk and credit risk management on accounting and market performances of banks operating in the Middle East and North Africa (MENA) region.Design/methodology/approachThis study uses a panel data regression analysis on a sample of 51 listed commercial banks operating in 10 MENA countries during the period 2010–2018.FindingsThe results show that credit risk management does not affect the accounting performance of banks, while it has a non-linear, convex relationship with market performance. Surprisingly, liquidity risk management is not a significant driver for either performance measure in studied banks. However, when a bank combines credit risk management with liquidity risk management efforts, liquidity risk management actions return significant results on both performances, illustrated by an inverted U-shaped relationship. In addition, this study examines the joint impact of both risks on bank performance. This study reveals that accounting and market performances are differently affected by joint risk management efforts. Their impact depends on the combination of risk management ratios upon which banks choose to focus their efforts.Practical implicationsThe findings help bankers and regulators further consider non-linearities and offer them new tools for managing the impact of credit and liquidity risk interactions towards achieving more financial stability.Originality/valueThese results contribute to traditional banking in offering bankers and regulators new tools for managing the impact of credit and liquidity risk interactions on bank performance.