Portfolio Credit Risk Research Articles

FACTOR COPULA MODEL FOR PORTFOLIO CREDIT RISK

A critical aspect in the valuation and risk management of multi-name credit derivatives is the modeling of the dependence among sources of credit risk. The dependence modeling poses difficulties in the pricing of a multi-name credit derivatives, in the estimation of the value-at-risk of a portfolio, or in the pricing of some other basket credit derivative as the description not only on the default arrival in an individual reference entity but on the default dependence among entities in the portfolio should be considered. Although the elliptical models have been widely used due to their mathematical tractability, the dependence modeling using the multi-dimensional Lévy process has shown growing interest among researchers despite its complexity. In this paper, we introduce one factor copula model for portfolio credit risk based on Normal Tempered Stable (NTS) distribution and calibrate the model through 5-year synthetic Collateralized Debt Obligation (CDO) tranche spreads under a large homogeneous portfolio approximation. The calibration results show that the one factor copula model based on NTS distribution is more flexible and provides a dependence structure fitting market CDO tranche spreads. As one of the major applications of the dependence modeling in credit risk, this model shares the advantage of the Gaussian one factor model, and all extensions and implementation methods used for it can be utilized.

Quantum speedup of Monte Carlo integration with respect to the number of dimensions and its application to finance

Monte Carlo integration using quantum computers has been widely investigated, including applications to concrete problems. It is known that quantum algorithms based on quantum amplitude estimation (QAE) can compute an integral with a smaller number of iterative calls of the quantum circuit which calculates the integrand, than classical methods call the integrand subroutine. However, the issues about the iterative operations in the integrand circuit have not been discussed so much. That is, in the high-dimensional integration, many random numbers are used for calculation of the integrand and in some cases similar calculations are repeated to obtain one sample value of the integrand. In this paper, we point out that we can reduce the number of such repeated operations by a combination of the nested QAE and the use of pseudorandom numbers (PRNs), if the integrand has the separable form with respect to contributions from distinct random numbers. The use of PRNs, which the authors originally proposed in the context of the quantum algorithm for Monte Carlo, is the key factor also in this paper, since it enables parallel computation of the separable terms in the integrand. Furthermore, we pick up one use case of this method in finance, the credit portfolio risk measurement and estimate to what extent the complexity is reduced.

ФОРМИРОВАНИE КРЕДИТНОГО ПОРТФЕЛЯ КОММЕРЧЕСКОГО БАНКА

In modern economic conditions, the formation of an optimal and high-quality loan port folio is the primary goal of a commercial bank, since the loan portfolio, on the one hand, generates the income of a commercial bank, and on the other, causes risks. This study substantiates the need to assess the formation of the loan portfolio, defines the methodological aspects of its implementation and analyzes the structure and dynamics of the loan portfolio of a regional commercial bank. The paper considers the method of sectoral diversification of corporate loans as a tool for reducing portfolio credit risk, since diversification involves the distribution of loans across various sectors of the economy. Based on this method, the calculation of the industry risk load of the credit portfolio of a commercial bank was carried out, which showed that to a greater extent the credit portfolio of the bank under study consists of industries with high credit risk. These circumstances are due to the fierce competition from the larger players in the banking market, so in the struggle for market share, regional banks are pursuing a risky credit policy, issuing loans to economically vulnerable borrowers. Their loan portfolios are diversified, but they are characterized by a high concentration of investments in such high-risk segments as trade and services, which is a risk-aggressive credit strategy. Based on the analysis of the profitability and risk of the loan portfolio, we conclude that there is a direct relationship between them. The yield of the loan portfolio has a negative trend, the level of risk of the loan portfolio is characterized by the percentage of the reserve for possible losses on loans, the value of which also decreased during the analyzed period.

Global Portfolio Credit Risk Management: The US Banks Post-Crisis Challenge

This paper addresses the problem of modeling credit risk for multi-product and global loan portfolios. The authors presented an improved version of the Basel Committee’s one-factor model for capital requirements calculation. They examined whether latent market factors corresponding to distinct portfolios are always highly correlated within the global portfolio and how this correlation impacts total losses distribution function. Historical losses of top-tier banks (JPMorgan Chace, Bank of America, Citigroup, Wells Fargo, US Bancorp) were analyzed. Furthermore, the estimation of the correlations between latent market factors was conducted, and its impact on the total loss distribution function was assessed. The research was performed based on consolidated financial statements for holding companies - FR Y-9C reports provided by the Federal Reserve Bank of Chicago. To verify the improved model, the authors analyzed two distinct loan portfolios for each bank, i.e., credit cards and commercial and industrial loans. They showed that the correlation between latent market factors could be significantly lower than one and disregarding this conclusion may lead to overestimating total unexpected losses. Hence, capital requirements calculated according to the IRB (Internal Ratings Based Approach) formula as a sum of individual VaR999 estimates may be biased. According to this finding, the enhanced one-factor model seems to be more accurate while calculating unexpected total loss for global portfolios. The authors proved that the active credit risk management process aiming to lower market factors’ correlation results in less volatile total losses. Therefore, financial institutions could be more resistant to macroeconomic downturns.

Credit risk in the microfinance industry: The role of gender affinity

ABSTRACT This study explores the role played by gender in lending transactions and specifically its effects on the loan portfolio credit risk of microfinance institutions (MFIs). Using a multicountry data set of developing countries, where MFIs mainly operate, the analysis shows that a higher proportion of female loan officers increases the loan portfolio at risk. Nonetheless, we also find that this positive relationship is negatively mediated by the gender affinity between female loan officers and female borrowers. Gender affinity suggests that female loan officers are more likely to lend to female borrowers, and this reduces the default rate of loans offered by MFIs.

Credit portfolio optimization: A multi-objective genetic algorithm approach

The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weighting and a matrix containing information about the default intensity of two stocks and the correlation in default between them. The default correlation and the default intensity are represented with a novel bivariate intensity model. A multi-objective genetic algorithm is introduced to optimize a credit portfolio with the purpose of overcoming limitations in the analytical method and improving the efficiency of optimization. The algorithm can be applied to a portfolio's credit risk management, which is particularly crucial for investors and regulars in emerging markets.

CREDIT RISK MANAGEMENT IN THE BANK’S FINANCIAL STABILITY SYSTEM

It is considered and updated the model of risk assessment of bank credit portfolio in the article. The profitability and risk are the main parameters of a bank loan portfolio. The ratio of these indicators characterizes the effectiveness of credit and general activity of the bank. The purpose of credit bank portfolio’s management is to ensure the highest yield at an acceptable risk of level. It is advisable to carry out the credit portfolio risk assessment in three stages. The first stage consist of using 9 indicators that are directly related to the occurrence of credit risk: credit activity ratio, reserve adequacy ratio, loan quality ratio, overdue loan ratio, maximum risk per one borrower (or group of borrowers), the concentration level of large credit risks, the concentration level of credit risk per one insider, loan ratio of written off from the reserve, the rate of return on credit operations. On the second stage of credit risk assessment, it is performed the scored assessment required to determine the integral credit risk indicator. Depending on whether the calculated indicator falls within the optimal value range, its scores are based on one of four formulas. On the third stage, there is determined the integral risk indicator and the degree of credit risk. The calculation of the integral credit risk indicator is advisable to use for assessment of credit risk in dynamics. Comparing the results of evaluating this indicator during several periods, we can conclude that there is a tendency to change the level of credit risk of a bank. It is analysed the credit risk indicators of JSC CB PrivatBank for 2016—2018 years. According to the evaluation results of the integral index, the authors concluded that the level of credit risk management in the bank was improved. But the values did not reach the maximum scores, so JSC CB PrivatBank is exposed to credit risks. It is proposed in the research a number of measures to optimize the credit risk management of bank.

ОЦІНКА КРЕДИТНОГО РИЗИКУ У ФІНАНСОВІЙ ДІЯЛЬНОСТІ БАНКУ

The purpose of the article is to study the current state of assessment and management of credit risk of banks, analysis of the regulatory framework governing credit risk management and the formation of reserves for active operations, as well as identifying ways to stimulate credit activity of banks in modern conditions. The main factors of credit risk by types of credit risks are presented. Particular attention is paid to individual and portfolio credit risk. The types of active operations on which determine the credit risk are defined. The general essence of commercial risk and directly credit risk of commercial banks is considered. Situations which can promote occurrence of credit risks in commercial banks are considered, and also methods and strategy of management by risks of commercial banks are developed. The article considers theoretical issues regarding the content and assessment of credit risk of the bank. The types of credit risk are identified and the characteristics of each of them are given. The complexity of the implementation and maintenance of such management systems is inversely proportional to the efficiency of the use of bank capital, that is, the more complex the method of calculating the risk – the less capital to be deducted on it. When calculating credit risk, the bank consolidates financial assets in accordance with the regulatory act - "Regulations on the determination of credit risk by banks of Ukraine for active banking operations." Indicators of EL, PD, LGD, EAD are assessed, which assess credit risk according to the regulations. The dynamics of credit risk standards of the banking system of Ukraine for 2009-2019 is considered. Statistics show that in 2019, almost all indicators of the loan portfolio are non-performing loans. Non-performing loans are loans that are doubtful or uncollectible until repayment. The quality of the loan portfolio with such a share of non-performing loans is a factor of inhibition and systemic risk. The calculations presented in the article show that state banks have the greatest credit risk, the share of non-performing loans does not decrease, even to 60%. Banks with a state share, not including Privatbank, are trying to reduce the level of lending risks. Recommendations for minimizing banking risk in financial activities are given.

Tail Dependence, Tail Asymmetry and Credit Portfolio Risk

Tail Dependence, Tail Asymmetry and Credit Portfolio Risk

Теоретико-методологічні відмінності диверсифікації та сек’юритизації кредитного портфеля банку

У статті досліджується сутність диверсифікації як основного методу управління кредитною діяльністю вітчизняних банків, під час використання якої враховується здатність портфеля кредитів зменшувати можливий ризик за умови врахування до нього різних за характеристиками та складом позик. У статті також зазначено, що диверсифікація дає змогу знизити не лише кредитний ризик, а й інші банківські ризики, які супроводжують загальний кредитний процес: ризик дострокового погашення, ліквідності, процентний, валютний та інші. Традиційна банківська теорія розглядає диверсифікацію як оптимальний спосіб ефективної організації кредитної діяльності, при цьому найефективнішою вважається така позикова діяльність банків, яка є максимально диверсифікованою. Проаналізовано, що кредитний портфель банків України слабо диверсифікований і дуже ризиковий, оскільки більше половини виданих кредитів вітчизняними банками зосереджено у Київській області та більше третини виданих позик знаходяться у торгівлі. У статті чітко виокремлено позитивні і негативні сторони диверсифікації. Завдяки дослідженню процесу сек’юритизації доведено, що для вітчизняних банків вона є відносно новим видом банківської діяльності. Завдяки трансформації неліквідних активів у ліквідні, сек’юритизація зменшує портфельний кредитний ризик банківської установи, що дає можливість розглядати сек’юритизацію як процес перетворення неліквідних активів у цінні папери, тобто як один зі специфічних механізмів здійснення фінансово-економічної діяльності банків. У статті пропонується розрізняти три основні різновиди сек’юритизації: класична сек’юритизація, синтетична сек’юритизація та сек’юритизація бізнесу. У статті зазначено причини, чому українські банки не поспішають застосовувати сек’юритизацію, а також її основні переваги порівняно з диверсифікацією активів.

The review of the open challenges in the IRB loan portfolio credit risk modeling

The Basel Committee on Banking Supervision finalized the Basel III accord in the December 2017 and launched the set of its standards – the Basel Framework – in December 2019. Both documents allow bank to use mathematical models for the credit risk estimation. There are quantitative and qualitative requirements for models to be allowed for use in the prudential regulation of banks. The approach is called an Internal-Ratings-Based one (IRB). This paper aims at discussing a set of issues related to IRB credit risk modeling and such model estimates use. Those issues include data pooling in the credit registries, applying copula-discriminant analysis, validating the borrower concentration per grade, assigning the hybrid credit rating, use of model estimates when voting at the credit committee, estimate of the ultimate credit risk-taking by banks.

A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*

ABSTRACTIn this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t‐copula. Moreover, we develop a three‐step importance sampling (IS) method for the t‐copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg–Marquardt algorithm to estimate the mean‐shift vector of the systematic risk factors after the probability measure change. Besides, we empirically examine the changes in the credit portfolio risks of 60 listed Chinese firms in different industries using our proposed method. The results show that our model can help the decision maker make the optimal operational decisions with total provision for risk, which hedges against the potential risk in the entire supply chain.

Random LGD adjustments in the Vasicek credit risk model

This paper proposes an approximate formula to measure the credit risk of portfolios under random recoveries. This formula is based on a Taylor expansion and enables having recoveries that are correlated with the default rates over the business cycle. We show how to calibrate the corresponding models and the accuracy of the approximation using defaulted corporate bonds data for the period 1982–2014. Our results show that the proposed formula can be used to approximate the loss distribution of a portfolio under random correlated recoveries in a very satisfactory way. Moreover, this kind of recovery models could be easily implemented under the Basel capital requirements regulation to improve the credit risk measurement.

Credit rating migration risk and interconnectedness in a corporate lending network

This study assesses the credit rating migration risk and interconnectedness in Japan's corporate lending market during the fiscal years 2008–2015. First, the study conducts a portfolio credit risk analysis by using outstanding lending data with borrowers’ and lenders’ names. The results show an expected shortfall with tail dependence of t-copula captures the heavy-tailed risk of Japanese institutions. The study also measures credit risk exposures and credit risk amounts by industry sector, and evaluates sector concentration risk. Subsequently, the study analyzes the network structure of lending contracts using network centrality measures. From the perspective of the network, institutions play a central role in terms of degree centrality. Further, the study undertakes a stress test using a historical economic scenario pertaining to a credit rating migration matrix shortly after the Lehman Brothers’ bankruptcy. The test finds a much sparser network structure due to a large number of firm defaults. The study's analysis offers banks and insurers important implications regarding credit risk management of corporate lending.

Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio

This work aims to illustrate an advanced quantitative methodology for measuring the credit risk of a loan portfolio allowing for diversification effects. Also, this methodology can allocate the credit capital coherently to each counterparty in the portfolio. The analytical approach used for estimating the portfolio credit risk is a binomial type based on a Monte Carlo Simulation. This method takes into account the default correlations among the credit counterparties in the portfolio by following a copula approach and utilizing the asset return correlations of the obligors, as estimated by rigorous statistical methods. Moreover, this model considers the recovery rates as stochastic and dependent on each other and on the time until defaults. The methodology utilized for coherently allocating credit capital in the portfolio estimates the marginal contributions of each obligor to the overall risk of the loan portfolio in terms of Expected Shortfall (ES), a risk measure more coherent and conservative than the traditional measure of Value-at-Risk (VaR). Finally, this advanced analytical structure is implemented to a hypothetical, but typical, loan portfolio of an Italian commercial bank operating across the overall national country. The national loan portfolio is composed of 17 sub-portfolios, or geographic clusters of credit exposures to 10,500 non-financial firms (or corporates) belonging to each geo-cluster or sub-portfolio. The outcomes, in terms of correlations, portfolio risk measures and capital allocations obtained from this advanced analytical framework, are compared with the results found by implementing the Internal Rating Based (IRB) approach of Basel II and III. Our chief conclusion is that the IRB model is unable to capture the real credit risk of loan portfolios because it does not take into account the actual dependence structure among the default events, and between the recovery rates and the default events. We underline that the adoption of this regulatory model can produce a dangerous underestimation of the portfolio credit risk, especially when the economic uncertainty and the volatility of the financial markets increase.

Importance sampling with splitting for portfolio credit risk

We consider a credit portfolio with highly skewed exposures. In the portfolio, small number of obligors have very high exposures compared to the others. For the Bernoulli mixture model with highly skewed exposures, we propose a new importance sampling scheme to estimate the tail loss probability over a threshold and the corresponding expected shortfall. We stratify the sample space of the default events into two subsets. One consists of the events that the obligors with heavy exposures default simultaneously. We expect that typical tail loss events belong to the set. In our proposed scheme, the tail loss probability and the expected shortfall corresponding to this type of events are estimated by a conditional Monte Carlo, which results in variance reduction. We analyze the properties of the proposed scheme mathematically. In numerical study, the performance of the proposed scheme is compared with an existing importance sampling method.

Methods for analyzing the quality of a banks loan portfolio

Lending is one of the major banking institutions. But lending has some risks of varying degrees. The main purpose of banks is to repay loans and to maximize profits. To do this, banks need to implement an efficient, flexible and modern credit portfolio quality management system. An important element of this system is the analysis of the quality of the loan portfolio. That is why the consideration of the methods by which banks can carry out this analysis is a very actual topic. The purpose of this paper is to review methods of analyzing the quality of a bank's loan portfolio, as well as to outline the disadvantages and benefits of each method. The paper examines the most common and modern approaches to defining the concept of «bank loan portfolio». The types of loan portfolio are considered. The definition of the quality of the bank loan portfolio is given. The definition of bank credit portfolio management is given and the basic elements of credit portfolio management are given. The main methods to be used in assessing the quality of a bank's loan portfolio are identified. They are divided into three groups: methods of expert judgment, statistical and analytical methods. A more detailed description of the methods in the three groups listed above is given. The rating method, the «Decision Tree» method, coefficient analysis, Monte Carlo method, scoring, correlation-regression analysis, taxonomic analysis and stress testing are characterized. The advantages and disadvantages of each method are also given. Indicators to assess the quality of the bank's loan portfolio are considered: the credit portfolio risk indicators and the profitability of credit operations. After the study, it was concluded that the above methods of analysis of the quality of the loan portfolio should be applied comprehensively. It is determined that currently the banks of Ukraine do not use the whole analytical set of methods, but choose for themselves several and constantly use them in the analysis of the quality of the loan portfolio. In order to ensure effective management of the bank's credit portfolio, it is necessary to constantly monitor the quality of the bank's credit portfolio for early detection of credit risk and its prevention, as well as for detection of deterioration of profitability indicators.

Impact of Credit Risk on Momentum and Contrarian Strategies: Evidence from South Asian Markets

We examine the profitability of the momentum and contrarian strategies in three South Asian markets, i.e., Bangladesh, India, and Pakistan. We also analyze, whether credit risk influences momentum and contrarian return for these markets from 2008 to 2014. We use default risk that relates to non-payments of debts by firms as a measure of credit risk. For that purpose, we use distance to default (DD) by Kealhofer, McQuown, and Vasicek (KMV) model as a proxy of credit risk. We calculate the credit risk and form the momentum and contrarian strategies of the firms based on high, medium, and low risk. We find that in all three markets, the momentum and contrarian returns are significant for medium and high credit risk portfolios and no momentum and contrarian returns for low credit risk portfolios.

Oil prices and bank credit risk in MENA countries after the 2008 financial crisis

PurposeThe purpose of this paper is to investigate the oil price–bank risk nexus by considering the heterogeneity of bank characters.Design/methodology/approachThis paper empirically tests the effect of oil price movements on bank credit risk by using a sample of 279 banks in the Middle East and North Africa countries from 2011 to 2017.FindingsAuthors find robust evidence that the credit risk of bank loan portfolios is negatively associated with increased oil prices. The heterogeneity analysis indicates that the effect of asset quality improvement brought about by rising oil prices is more salient in conventional banks, and banks with small size, low liquidity and whose funding source relies on customers’ deposits.Practical implicationsThe results favor the diversification of bank funding sources, the improvement of a country’s financial development, the adoption of explicit deposit insurance and macroprudential policies, such as countercyclical liquidity buffers, to weaken the adverse impact of oil prices declines.Originality/valueThe present paper enriches the literature of oil price–bank risk nexus by analyzing the heterogeneity of bank characters and advances our knowledge on the determined factors of bank riskiness and vulnerability.

Problems of qualitative evaluation of commercial bank loan

The article defines the nature of the loan portfolio, as well as the problems in the assessment and analysis of the commercial bank loan portfolio. In order to improve the existing credit portfolio of the bank, the dynamics, categories of the borrower ratio and the quality of the loan portfolio are analyzed, based on the obtained data, significant factors influencing the formation and management of the analyzed bank's loan portfolio are determined. Generation of a loan portfolio is usually subject to issuance of loans with maximum yield on the same terms. The profitability of a loan transaction is determined by the amount of the interest rate on the specific loan, the duration of the loan and the accepted system of calculation of interest payments. Practice shows that the success of managing the efficiency of banking investments and the formation of an effective structure of the bank's credit portfolio depend to a large extent on the skills of managing the banking institutions that carry out optimal formation, management of the credit portfolio and good management to ensure the efficiency of activities with the least possible risk and maximize results. The quality of the loan portfolio is analyzed using the coefficient method based on the calculation and analysis of trends in the development of relevant quantitative indicators. In the economic literature, the indicators that we can use to assess the quality of a bank's loan portfolio are divided into two groups: the credit portfolio risk index and the profitability of the credit business. The results of the analysis allow us to draw conclusions about the level of risk and profitability of the bank's loan portfolio and to develop measures to improve the effectiveness of the bank's credit policy. Based on the analysis of the structure and quality of the loan portfolio, the Bank's management is given an opinion to consider in order to determine the priority instructions for the placement of credit resources, limit the concentration and diversification of the loan portfolio and set limits on the execution of individual loans. It is concluded that prudent credit policy with an increase in rates, constant monitoring of the components of the credit portfolio, urgent management measures of banking institutions in the field of risk management should lead to an increase in lending and improve the quality of the portfolio management system. It is noted that the implementation of mainly quality measures to improve the structure of the loan portfolio will inevitably affect the financial stability of the bank and business efficiency, as well as increase its competitiveness and operational security.