Internal Ratings-based Approach Research Articles

EXPRESS: Seller-Orchestrated Inventory Financing under Bank Capital Regulation

EXPRESS: Seller-Orchestrated Inventory Financing under Bank Capital Regulation

Parametric methods for precision calibration of scoring models

Once a scoring model has been developed for use in assessing a borrower’s credit risk, under the internal ratings-based (IRB) approach, it must be calibrated to a real-world measure of default frequency. The conservativeness of the calibration is tightly controlled, if it is not violated in the allowed number of digits of the rating scale, only then the model is authorized for use. If violated, the calibration probability of default must be raised, placing additional strain on the bank’s capital and reserves. In the presented paper, two new methods to improve the calibration accuracy are proposed. The methods have been tested in practice and provide significantly positive results in certain segments of the loan portfolio.

Back to the roots of internal credit risk models: Does risk explain why banks' risk-weighted asset levels converge over time?

The internal ratings-based (IRB) approach maps bank risk profiles more adequately than the standardized approach. After switching to IRB, banks' risk-weighted asset (RWA) densities are thus expected to diverge, especially across countries with different supervisory strictness and risk levels. However, when examining 52 listed banks headquartered in 14 European countries that adopted the IRB approach, we observe a convergence of their RWA densities over time. We test whether this convergence can be entirely explained by differences in the size of the banks, loss levels, country risk, and/or time of IRB implementation. Our findings indicate that this is not the case. Whereas banks in high-risk countries with lax regulation, reduce their RWA densities, banks elsewhere increase theirs. Especially for banks in high-risk countries, RWA densities underestimate banks' economic risk. Hence, the IRB approach enables regulatory arbitrage, whereby authorities only enforce strict supervision on capital requirements if they do not jeopardize bank viability.

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.

Enriching the green economy through sustainable investments: An ESG-based credit rating model for green financing

Environmental, social, and governance (ESG) measures have grown significantly as sustainable investment has become a key driver of capital allocation. Sustainable investment is one of the main agendas for fulfilling sustainable development. Thus, financial institutions can significantly tackle socio-ecological concerns by recognising socially conscientious borrowers for long-term investments. They can invest in or lend to enterprises involved in sustainable development to construct a sustainable future. Unfortunately, financial institutions confront several obstacles in selecting such borrowers from a large pool of applicants. To accomplish the goal of sustainable investments, this study proposes an ESG-based credit rating model that considers a firm's ESG performance. The suggested model was built by applying the fuzzy Best Worst Method (BWM) and the newly developed fuzzy Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) Sorting. The fuzzy BWM was used to determine the weight of criteria, while the fuzzy TOPSIS-Sorting was used to evaluate firms against the identified criteria. A practical case has been demonstrated to show the utility of the proposed model. This study identifies the financial pillar as the most important, accounting for 43% of the overall importance, followed by the environmental pillar (24%), the social pillar (19%), and the governance pillar (14%). The suggested credit rating model has shown an accuracy rate of 84.31% and a true positive rate of 87.5%. Regarding policy implementations, financial institutions, regulators, and other authorities may employ it to assist sustainable investments in fulfilling sustainable development goals. Banks may use the suggested method to calculate the capital required under the internal rating-based approach of Basel norms.

Эмпирический анализ предельных уровней дефолта для признания подхода на основе внутренних рейтингов сомнительным при оценке кредитного риска

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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.

Credit risk measures and the estimation error in the ASRF model under the Basel II IRB approach

In many standard derivation and presentations of risk measures like the Value-at-Risk or the Expected Shortfall, it is assumed that all the model’s parameters are known. In practice, however, the parameters must be estimated and this introduces an additional source of uncertainty that is usually not accounted for. The Prudential Regulators have formally raised the issue of errors stemming from the internal model estimation process in the context of credit risk, calling for margins of conservatism to cover possible underestimation in capital. Notwithstanding this requirement, to date, a solution shared by banks and regulators/supervisors has not yet been found. In our paper, we investigate the effect of the estimation error in the framework of the Asymptotic Single Risk Factor (ASRF) model that represents the baseline for the derivation of the credit risk measures under the Internal-Ratings Based (IRB) approach. We exploit Monte Carlo simulations to quantify the bias induced by the estimation error and we explore an approach to correct for this bias. Our approach involves only the estimation of the long run average probability of default and not the estimation of the asset correlation given that, in practice, banks are not allowed to modify this parameter. We study the stochastic characteristics of the probability of default estimator that can be derived from the Asymptotic Single Risk Factor framework and we show how to introduce a correction to control for the estimation error. Our approach does not require introducing in the Asymptotic Single Risk Factor model additional elements like the prior distributions or other parameters which, having to be estimated, would introduce another source of estimation error. This simple and easily implemented correction ensures that the probability of observing an exception (i.e. a default rate higher than the estimated quantile of the default rate distribution) is equal to the desired confidence level. We show a practical application of our approach relying on real data.

Valuating consumer credit portfolios

This paper proposes a model that associates borrower credit risk with the cash flow method to assess the economic value of a consumer credit portfolio. A Monte Carlo simulation applying the method to an illustrative loan reveals that the lending standards of the institution, captured in the model by the expected and unexpected losses of the contract according to Basel II Internal Rating-Based Approach, is a key driver of the portfolio's intrinsic value, lending support to the evidence that a bank's credit policy and a bank's valuation are associated.

Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction

Implementing new machine learning (ML) algorithms for credit default prediction is associated with better predictive performance; however, it also generates new model risks, particularly concerning the supervisory validation process. Recent industry surveys often mention that uncertainty about how supervisors might assess these risks could be a barrier to innovation. In this study, we propose a new framework to quantify model risk-adjustments to compare the performance of several ML methods. To address this challenge, we first harness the internal ratings-based approach to identify up to 13 risk components that we classify into 3 main categories—statistics, technology, and market conduct. Second, to evaluate the importance of each risk category, we collect a series of regulatory documents related to three potential use cases—regulatory capital, credit scoring, or provisioning—and we compute the weight of each category according to the intensity of their mentions, using natural language processing and a risk terminology based on expert knowledge. Finally, we test our framework using popular ML models in credit risk, and a publicly available database, to quantify some proxies of a subset of risk factors that we deem representative. We measure the statistical risk according to the number of hyperparameters and the stability of the predictions. The technological risk is assessed through the transparency of the algorithm and the latency of the ML training method, while the market conduct risk is quantified by the time it takes to run a post hoc technique (SHapley Additive exPlanations) to interpret the output.

Credit Line Exposure at Default Modelling Using Bayesian Mixed Effect Quantile Regression

AbstractFor banks, credit lines play an important role exposing both liquidity and credit risk. In the advanced internal ratings-based approach, banks are obliged to use their own estimates of exposure at default using credit conversion factors. For volatile segments, additional downturn estimates are required. Using the world's largest database of defaulted credit lines from the US and Europe and macroeconomic variables, we apply a Bayesian mixed effect quantile regression and find strongly varying covariate effects over the whole conditional distribution of credit conversion factors and especially between United States and Europe. If macroeconomic variables do not provide adequate downturn estimates, the model is enhanced by random effects. Results from European credit lines suggest that high conversion factors are driven by random effects rather than observable covariates. We further show that the impact of the economic surrounding highly depends on the level of utilization one year prior default, suggesting that credit lines with high drawdown potential are most affected by economic downturns and hence bear the highest risk in crisis periods.

Shadow Banking Under Capital Regulation and The Internal Ratings-Based Approach

The article discusses the mechanism of risk-shifting in the presence of information asymmetry. Due to the asymmetry of information, the regulator cannot differentiate bank assets by the risk level, and therefore cannot guarantee the banking system’s solvency. This restrictive policy encourages the banks to work outside of regulation, using credit intermediation or other schemes that are not imposed by regulation. The paper suggests the IRB approach, as a tool to reduce information asymmetry that affects shadow banking size. The modification of the model Ordoñez (2018) is proposed. It presented the critical levels of the capital requirements at which banks choose to stay in shadow banking.

Bank-sourced transition matrixes: are banks’ internal credit risk estimates Markovian?

This study explores banks’ internal credit risk estimates and the associated banksourced transition matrixes. We empirically test the widely used Markovian property and time homogeneity assumptions at an unprecedented scale. Our unique data set consists of internal probability of default estimates from 12 global banks that employ an advanced internal ratings-based approach, covering monthly observations on 20,000 corporates over the period 2015–18. The results indicate that internal credit risk estimates do not satisfy the two assumptions, showing evidence of both path dependency and time heterogeneity even within the period of economic expansion. Banks tend to revert their rating actions, which contradicts previous findings based on data from credit rating agencies. This has significant practical implications through bank-sourced credit transition matrixes, which are becoming increasingly important as regulators begin to use more detailed credit risk data sets (eg, analytical credit data sets by the European Central Bank) with potential applications in stress testing.

Eurozone Sovereign Default Risk and Capital: A Bayesian Approach

Using a Bayesian generalized linear mixed model (GLMM), we analyze Eurozone sovereign real-world default probabilities and correlations, and compare regulatory and economic capital requirements. The approach combines prior information and sparse sovereign historical default data. One main finding is that capital under the Basel internal ratings based approach (IRBA) is higher than under the standardized approach (SA) by a factor of 2.06 to 8.86, depending on the method for estimating the probability of default. This divergence is driven mainly by zero capital charges for highly rated securities under the SA. Furthermore, under the Bayesian model, Basel IRBA capital is roughly equivalent to economic capital using the expected shortfall at a 99% confidence level. The results suggest that the zero risk weights under the SA are not consistent with economic risk and offer opportunities for regulatory arbitrage.

Financial Ratios : A Tool for Computing Probability of Corporate Default

Financial institutions want to draw upon a pool of borrowers having a high capacity of making repayments to ensure a smooth lending process. Before lending, financial institutions can assess their prospective customers using various tools such as customer interviews, visits, ratings from external credit rating agencies, financial analysis, and internal ratings along with risk mitigation through securities and collateral, as guided by the regulators and Basel committee. Basel accord suggests an internal rating-based approach where banks are allowed to estimate the borrower’s probability of default, internally. Multivariate statistical approaches such as logistic models is widely used for finding the probability of default models by banks. The current study covers the development of probability of default model after using financial ratios as predictors. Results reveal that financial ratios have a significant impact on the firm’s probability of default, except cash flow ratios. Fitted probability of default model can be utilized in any corporate lending firm for the assessment of its strength and its ability to pay back the loan. Probability of default model meets the State Bank of Pakistan (SBP) and Basel requirements for the implementation of internal rating based approaches in the case of conventional Pakistani banks. Credit risk rating and expected credit losses can be calculated using fitted probability of default model. This study was executed only based on financial ratios because of the availability of financial data only. Conventional banks use managerial, business and economic factors along with financial ratios to construct better predictive models. The current study was conducted on limited data adopted from financial statements analysis published by SBP. The developed model in this study is suitable for all non-financial corporates. Future researchers may develop different models for different industries, for instance, manufacturing and trading.Keywords: financial ratios, default, Basel, internal rating

Basel regulatory capital formula revised

Credit capital requirement is a key component of Basel implementation to assess a bank’s capital adequacy. Under the Internal Rating-Based approach, some risk parameters, including Asset Correlation, are implicit assumptions that cannot be observed directly. While some heuristic formulae of Asset Correlation for different business segments are provided by Basel, they may not be fully consistent with each bank’s loss experience and thus may cause systematic underestimation of banks’ capital requirement. To address this issue, we derive an equivalent capital formula in such way that the unobservable Asset Correlation is replaced by an observable and well-understood parameter called Default Volatility, which can be calibrated based on banks’ historical loss experience. This new approach simplifies parameter estimation process without requiring additional data, as well as making risk analysis such as stress testing more credible.

Did Basel regulation cause a significant procyclicality?

This paper examines the procyclical effect of risk-sensitive capital regulation on bank lending. We find evidence that the sensitivity of bank lending to GDP is significantly positive under the internal rating-based approach. Our findings show that the risk-sensitive requirements of the Basel II and III regulations have procyclical effects on bank lending in nine European countries. The introduction of the risk-sensitive capital requirement rule negatively impacts lending in these countries. The policy implication is that regulators should place greater priority on building a buffer in advance, which can be used in times of stress rather than for dampening excess cyclicality.

Improving regulatory capital allocation: a case for the internal ratings-based approach for retail credit risk exposures

PurposeThe purpose of this study is to demonstrate that the internal ratings-based (IRB) approach provides more effective risk discrimination than the standardized approach when calculating regulatory capital for retail credit risk exposures.Design/methodology/approachThe author uses four retail credit data sets to compare regulatory capital appropriation using the IRB approach and the standardized approach. The author follows the regulatory capital calculation method recommended under Basel III. For the IRB approach, the author uses a logistic regression to determine the probability of default.FindingsThe results suggest that the IRB approach provides more effective risk discrimination across individual exposures, which allows more regulatory capital to be held against riskier exposures and less regulatory capital to be held against less risky exposures. The author further argues that the Basel III output floor, as presently constructed, may disincentivize the use of the IRB approach and further diminish the value of secured lending under the IRB approach. To address this issue, the author offers two simple adjustments to the current design of the output floor.Originality/valueWhile studies have argued the idea of risk-sensitive regulatory capital, the author has not observed any research that empirically compares the risk-sensitivity of regulatory capital across retail credit exposures, which makes up a significant portion of many banks’ credit exposures. This study also highlights what appears to be a major point of concern for the output floor, which is set to be phased in starting January 2022. This is of particular value because this point has not appeared to receive any attention in the literature thus far.

Risk-sensitive Basel regulations and firms’ access to credit: Direct and indirect effects

This paper examines the impact of risk-sensitive Basel regulations on debt financing of firms around the world. It investigates how firms cope with the impact through adjustments to their financing sources and capital investments. We find that the implementation of Basel II regulations is associated with reduced credit availability for lower-rated firms. Such firms mitigate the shortage in bank credit through increased reliance on accounts payable, lower payouts to shareholders, and reduced capital investments. The impact of the capital regulation is lower in countries that rely on the internal ratings-based approach. The key results are robust to controls for banking crises, bank-specific controls, and the inclusion of loan-level information. The findings of this paper substantially contribute to the understanding of the real effects of risk-sensitive bank capital regulations.

Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation

In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We compare the statistical performance of a simple and traditionally used model like the Logistic Regression (Logit), with more advanced ones like Lasso penalized logistic regression, Classification And Regression Tree (CART), Random Forest, XGBoost and Deep Neural Networks. Following the process deployed for the supervisory validation of Internal Rating-Based (IRB) systems, we examine the benefits of using ML in terms of predictive power, both in classification and calibration. Running a simulation exercise for different sample sizes and number of features we are able to isolate the information advantage associated to the access to big amounts of data, and measure the ML model advantage. Despite the fact that ML models outperforms Logit both in classification and in calibration, more complex ML algorithms do not necessarily predict better. We then translate this statistical performance into economic impact. We do so by estimating the savings in regulatory capital when using ML models instead of a simpler model like Lasso to compute the risk-weighted assets. Our benchmark results show that implementing XGBoost could yield savings from 12.4% to 17% in terms of regulatory capital requirements under the IRB approach. This leads us to conclude that the potential benefits in economic terms for the institutions would be significant and this justify further research to better understand all the risks embedded in ML models.