Capital Requirements For Credit Risk Research Articles

Measurement and Calibration of Regulatory Credit Risk Asset Correlations

Vasicek’s asymptotic single risk factor (ASRF) model is employed by the Basel Committee on Banking Supervision (BCBS) in its internal ratings-based (IRB) approach for estimating credit losses and regulatory credit risk capital. This methodology requires estimates of asset correlations; these are prescribed by the BCBS. Practitioners are interested to know market-implied asset correlations since these influence economic capital and lending behavior. These may be backed out from ASRF loan loss distributions using ex post loan losses. Prescribed asset correlations have been neither updated nor recalibrated since their introduction in 2008 with the implementation of the Basel II accord. The market milieu has undergone significant alterations and adaptations since then; it is unlikely that these remain relevant. Loan loss data from a developed (US) and developing (South Africa) economy spanning at least two business cycles for each region were used to explore the relevance of the BCBS calibration. Results obtained from three alternative methodologies are compared with prescribed BCBS values, and the latter were found to be countercyclical to empirical loan loss experience, resulting in less punitive credit risk capital requirements than required in market crises and more punitive requirements than required in calm conditions.

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

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

Werden sichere Finanzierungen unattraktiv?

On December 7th 2017, the Basel Committee on Banking Supervision (BCBS) published the first completed draft of the finalisation of the Basel III regulation for credit institutions. One of the BCBS's main focuses in this revision was on the calculation of capital requirements for credit risk using internal models. In order to limit that part of the risk-weighted asset variability which is not intended from a regulatory perspective, banks will have to consider input floors and an output floor. These rules are attributed to significant - partly intended, partly unintended - consequences, especially for traditionally low-risk financing. Using the example of real estate and lease financing, this study illustrates that, in addition to the intended consequences, the new regulatory requirements can also incentivize banks to shift their business in favor of riskier financing.

M‐PRESS‐CreditRisk: Microprudential and Macroprudential Capital Requirements for Credit Risk under Systemic Stress

AbstractM‐PRESS‐CreditRisk is a novel stress testing approach that can help authorities gauge banks' capital adequacy related to credit risk. For the first time, it combines the assessment of microprudential capital requirements under Pillars 1 and 2 and macroprudential buffers in a unified, coherent framework. Its core element is an advanced credit portfolio model—SystemicCreditRisk—built upon a rich, nonlinear dependence structure for correlated bank portfolios. The model is applied to a sample of 12 systemically important German banking groups and delivers measures for systemic credit risk and the banks' contributions to it in both baseline and stress scenarios.

Interest Rate Swap Credit Value Adjustment

The credit valuation adjustment (CVA) of OTC derivatives is an important part of the Basel III credit risk capital requirements and current accounting rules. Its calculation is not an easy task - not only it is necessary to model the future value of the derivative, but also the probability of default of a counterparty. Another complication arises in the calculation when the exposure to a counterparty is adversely correlated with the credit quality of that counterparty, i.e. when it is needed to incorporate the wrong-way risk. A semi-analytical CVA formula simplifying the interest rate swap (IRS) valuation with the counterparty credit risk including the wrong-way risk is derived and analyzed in the paper. The formula is based on the fact that the CVA of an IRS can be expressed using swaption prices. The link between the interest rates and the default time is represented by a Gaussian copula with constant correlation coefficient.Finally, the results of the semi-analytical approach are compared with the results of a complex simulation study.

The disparity in PD and LGD estimates within the IRB framework and prospects for future improvement

The internal ratings-based approach (IRB) to calculating capital requirements for credit risk was the main novelty brought about by Basel II and remains fundamentally unchanged in Basel III. The adequacy of this framework has been controversial since its inception, but throughout the years, most regulators and the banking industry have remained among its strong supporters. This paper focuses on some of the most problematic elements of the IRB approach, concerning the estimation of the main parameters capital requirements depend on and the difficulty embedded in the validation process. In the light of these difficulties, it concludes maintaining the IRB approach in its current form and scope reflects an overly optimistic stance with respect to financial institutions’ ability to accurately estimate critical risk parameters and prospects for future improvements.

Including Defaults on New Loans in Capital Requirements for Credit Risk

The usual application of the Basel formula for calculating risk-weighted assets (RWA) for credit risk involves estimating losses over the following year on loans in a bank's portfolio at the beginning of the year (the 'back-book'). In practice, the bank will also suffer some losses from new loans that entered during the year (the 'front book') so, if the formula gives an unbiased estimate of expected losses from the back book, it will under-estimate losses from the whole portfolio. To take account of losses from the front book, it is necessary to adjust the calculation, taking account of the inflow rate of new loans. This can be done by either of two broadly equivalent techniques: (1) multiply the RWA for loans in each internal rating grade pool by (one plus half the expected inflow rate of new loans to the pool); or (2) rearrange the Basel RWA formula so that it has three distinct places where PD is used, and replace the value that is used in the dominant one of those places (the `primary PD slot') by an estimate of the unconditional expected portfolio default rate, rather than of individual loan default probability. Making these adjustments will give an unbiased estimate of total defaults in a downturn year, provided the original calculation gives an unbiased estimate of defaults from the back-book. A simpler technique of estimating front-book-inclusive default rates is to remove half the exits from the denominator in calculating observed default rates. This approximation can be reasonably accurate provided that both inflow and exit rates, excluding grade migrations, are low for each grade to which it is applied. That may make it unsuitable for the riskiest grades, but in general it is fairly accurate.

Did the bank capital relief induced by the Supporting Factor enhance SME lending?

The introduction of the SME Supporting Factor (SF) allows banks to reduce capital requirements for credit risk on exposures to SME. This means that banks can free up capital resources that can be redeployed in the form of new loans. Our study documents that the SF alleviates credit rationing for medium-sized firms that are eligible for the application of the SF but not for micro/small firms. These results suggest that European banks were aware of this policy measure and optimized both their regulatory capital and their credit exposures by granting loans to the medium-sized firms, which are safer than micro/small firms.

Did the Bank Capital Relief Induced by the Supporting Factor Enhance SME Lending?

The introduction of the SME Supporting Factor (SF) allows banks to reduce capital requirements for credit risk on exposures to firms with a turnover of below EUR 50 million. This means that banks can free up capital resources that can be redeployed in the form of new loans. Our study documents that the SF alleviates credit rationing for medium-sized firms that are eligible for the application of the SF but not for micro/small firms. These results suggest that European banks were aware of this policy measure and optimized both their regulatory capital and their credit exposures by granting loans to the safest SMEs. Several extensions are used to isolate the effects of the SF on SME lending and to make them clearly visible.

Credit risk capital requirements under basel III norms by selected banks in India

Paper compares credit risk capital requirements by selected banks under BASEL III norms. Nine banks, three in each category were selected. Data was collected for 9 years i.e 6 years before and 3 years after the adoption of Basel III norms. It was found out that private sector banks are superior on complying with the new norms as compared to nationalised and foreign banks. Enhanced capital requirements have witnessed a positive trend in private sector banks and few cases foreign banks only. It was also observed that selected banks are using standardised approach for calculating their credit risk capital requirements.

Compliance of Basel III Norms: Comparison of selected Nationalised, Private and Foreign Banks

Paper compares compliance of BASEL III norms as given by RBI by selected nationalized, private and foreign banks. There are three categories of banks in India-Public, Private and Foreign banks. Nine banks, three in each category were selected. Data related to 3 factors of Basel III norms i.e., Capital Adequacy requirements, Supervisory Review and Market Discipline for past 10 years i.e.3 years for Basel I norms, 6 years before and 3 years after the adoption of Basel III norms was collected. It was found out that significant difference was observed in case of Tier 1 capital under Basel II and Basel III norms because the capital of the banks had to be raised in order to meet the requirements of new capital adequacy norms. Significant difference was also observed in case of CRAR of the nationalised and private sector banks. Total capital adequacy ratio of the banks was mainly affected by risk weighted assets of the banks. The more the risk weighted assets of the bank the higher is the capital to risk weighted assets ratio (CRAR). The credit risk capital requirements of private sector banks, market risk capital requirements of nationalised banks and operational risk capital requirements of private sector banks witnessed significant differences under Basel II norms and Basel III norms.

'Support Is Appreciated': On the Effectiveness of the SME Supporting Factor

The introduction of the SME Supporting Factor (SF) allows banks to reduce capital requirements for credit risk on exposures to firms with a turnover of below EUR 50 million. This means that banks can free up capital resources that can be redeployed in the form of new loans. Our study documents that the SF alleviates credit rationing for medium-sized firms that are eligible for the application of the SF but not for micro/small firms. These results suggest that European banks were aware of this policy measure and optimized both their regulatory capital and their credit exposures by granting loans to the safest SMEs. Several extensions are used to isolate the effects of the SF on SME lending and to make them clearly visible.

English

Based on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, the study shows robustness of the Semi-Markovian models in forecasting Probabilities of Default and Loss Given Default for a portfolio of consumer loans. The study models credit risk as a reliability problem on the basis of which we generate credit risk indicators and quantify prospective capital holding based on forecast delinquencies. Consumer ratings are based on Monte-Carlo simulation techniques and the initial probability transition matrix on the Merton model. Banks could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans.   Key words: Semi-Markov models, credit risk, Central Bank of Kenya.

Measuring Concentration Risk - A Partial Portfolio Approach

Concentration risk is an important feature of many banking sectors, especially in emerging and small economies. Under the Basel Framework, Pillar 1 capital requirements for credit risk do not cover concentration risk, and those calculated under the Internal Ratings Based (IRB) approach explicitly exclude it. Banks are expected to compensate for this by autonomously estimating and setting aside appropriate capital buffers, which supervisors are required to assess and possibly challenge within the Pillar 2 process. Inadequate reflection of this risk can lead to insufficient capital levels even when the capital ratios seem high. We propose a flexible technique, based on a combination of “full” credit portfolio modeling and asymptotic results, to calculate capital requirements for name and sector concentration risk in banks’ portfolios. The proposed approach lends itself to be used in bilateral surveillance, as a potential area for technical assistance on banking supervision, and as a policy tool to gauge the degree of concentration risk in different banking systems.

Measuring Concentration Risk - A Partial Portfolio Approach

Concentration risk is an important feature of many banking sectors, especially in emerging and small economies. Under the Basel Framework, Pillar 1 capital requirements for credit risk do not cover concentration risk, and those calculated under the Internal Ratings Based (IRB) approach explicitly exclude it. Banks are expected to compensate for this by autonomously estimating and setting aside appropriate capital buffers, which supervisors are required to assess and possibly challenge within the Pillar 2 process. Inadequate reflection of this risk can lead to insufficient capital levels even when the capital ratios seem high. We propose a flexible technique, based on a combination of 'full' credit portfolio modeling and asymptotic results, to calculate capital requirements for name and sector concentration risk in banks' portfolios. The proposed approach lends itself to be used in bilateral surveillance, as a potential area for technical assistance on banking supervision, and as a policy tool to gauge the degree of concentration risk in different banking systems.

Interest Rate Swap Credit Valuation Adjustment

The credit valuation adjustment (CVA) of OTC derivatives is an important part of the Basel III credit risk capital requirements and current accounting rules. Its calculation is not an easy task—not only is it ­necessary to model the future value of the derivative, but also the probability of the default of a counterparty. Another complication in the calculation arises when the exposure to a counterparty is adversely, or favourably, correlated with the credit quality of that counterparty, i.e., when it is necessary to incorporate wrong-way, or right-way, risk. A semi-analytical CVA formula simplifying interest rate swap (IRS) valuation with the counterparty credit risk including the wrong-way risk is derived and analyzed in the article. The formula is based on the fact that the CVA of an IRS can be expressed using swaption prices. The link between the interest rates and the default time is represented by a Gaussian copula with a constant correlation coefficient. Finally, the results of the semi-analytical approach are compared with the results of a complex simulation study.

Estimating loss-given default through advanced credibility theory

The New Basel Accord allows internationally active banking organizations to calculate their credit risk capital requirements using an internal ratings based approach, subject to supervisory review. One of the modeling components is the loss-given default (LGD): it represents the credit loss for a bank when extreme events occur that influence the obligor ability to repay his debts to the bank. Among researchers and practitioners the use of statistical models such as linear regression, Tobit or decision trees is quite common in order to compute LGDs as a forecasting of historical losses. However, these statistical techniques do not seem to provide robust estimation and show low performance. These results could be driven by some factors that make differences in LGD, such as the presence and quality of collateral, timing of the business cycle, workout process management and M&A activity among banks. This paper evaluates an alternative method of modeling LGD using a technique based on advanced credibility theory typically used in actuarial modeling. This technique provides a statistical component to the credit and workout experts’ opinion embedded in the collateral and workout management process and improve the predictive power of forecasting. The model has been applied to an Italian Bank Retail portfolio represented by Overdrafts; the application of credibility theory provides a higher predictive power of LGD estimation and an out-of-time sample backtesting has shown a stable accuracy of estimates with respect to the traditional LGD model.

Concentration risk model for Greek bank's credit portfolio

Purpose – The IRB capital requirements of Basel II define the minimum level of capital that the bank has to retain to cover the current risks of its portfolio. The major risk that many banks are facing is credit risk and Basel II provides an approach to calculate its capital requirement. It is well known that Pillar I Basel II approach for credit risk capital requirements does not include concentration risk. The paper aims to propose a model modifying Basel II methodology (IRB) to include name concentration risk. Design/methodology/approach – The model is developed on data based on a portfolio of Greek companies that are financed by Greek commercial banks. Based on the initial portfolio, new portfolios were simulated having a range of different credit risk parameters. Subsequently, the credit VaR of various portfolios was regressed against the credit risk indicators such as Basel II capital requirements, modified Herfindahl Index and a non-linear model was developed. This model modifies the Pillar I IRB capital requirements model of Basel II to include name concentration risk. Findings – As the Pillar I IRB capital requirements model of Basel II does not include concentration risk, the credit VaR calculations performed in the present work appeared to have gaps with the Basel II capital requirements. These gaps were more apparent when there was high concentration risk in the credit portfolios. The new model bridges this gap providing with a correction coefficient. Practical implications – The credit VaR of a loan portfolio could be calculated from the bank easily, without the use of additional complicated algorithms and systems. Originality/value – The model is constructed in such a way as to provide an approximation of credit VaR satisfactory for business loan portfolios whose risk parameters lie within the range of those in a realistic bank credit portfolio and without the application of Monte Carlo simulations.

Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis.

Forecasting bank loans loss-given-default

With the advent of the new Basel Capital Accord, banking organizations are invited to estimate credit risk capital requirements using an internal ratings based approach. In order to be compliant with this approach, institutions must estimate the loss-given-default, the fraction of the credit exposure that is lost if the borrower defaults. This study evaluates the ability of a parametric fractional response regression and a nonparametric regression tree model to forecast bank loan credit losses. The out-of-sample predictive ability of these models is evaluated at several recovery horizons after the default event. The out-of-time predictive ability is also estimated for a recovery horizon of 1 year. The performance of the models is benchmarked against recovery estimates given by historical averages. The results suggest that regression trees are an interesting alternative to parametric models in modeling and forecasting loss-given-default.