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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Robert Stewart
Keyword(s):  
Credit Risk ◽  
Capital Allocation ◽  
Basel Iii ◽  
Data Sets ◽  
Regulatory Capital ◽  
Content Type ◽  
Retail Credit ◽  
Current Design ◽  
Credit Data ◽  
Irb Approach

Purpose The 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/approach The 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. Findings The 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/value While 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.

scholarly journals Credit Risk Management of Property Investments through Multi-Criteria Indicators

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 106
Author(s):  
Marco Locurcio ◽  
Francesco Tajani ◽  
Pierluigi Morano ◽  
Debora Anelli ◽  
Benedetto Manganelli
Keyword(s):  
Real Estate ◽  
Credit Risk ◽  
Risk Index ◽  
Capital Requirements ◽  
Basel Iii ◽  
Participatory Process ◽  
Regulatory Capital ◽  
The Real Estate ◽  
Financial Variables ◽  
Synthetic Index

The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur.

What explains the recourse of US commercial banks to securitization?

2016 ◽  
Vol 15 (3) ◽  
pp. 317-328 ◽  
Author(s):  
Nesrine Bensalah ◽  
Hassouna Fedhila
Keyword(s):  
Credit Risk ◽  
Selection Procedure ◽  
Regulatory Capital ◽  
Heckman Selection Model ◽  
Mortgage Loans ◽  
Content Type ◽  
Risk Transfer ◽  
Credit Risk Transfer ◽  
The Usa ◽  
Heckman Selection

Purpose The purpose of this paper is to investigate the reasons that urge US banks to securitize. Design/methodology/approach The authors apply a logistic regression model to a sample of 5,394 observations. The dependent variable takes 1 if the bank securitizes and 0 if not. The authors use also, a Heckman selection model to account for the potential dependence between the decision to securitize and the decision of which assets to securitize. Findings The results indicate that liquidity, credit risk transfer, regulatory capital arbitrage and profitability are the most important factors that drive securitization in the USA. Moreover, the nature of the asset securitized appears to be dependent on the objective that the bank pursues. For funding and capital arbitrage objectives, the bank needs to securitize its mortgage loans. However, for credit risk transfer purposes, it has to opt for a non mortgage securitization. The nature of the asset securitized can thus, be used as a signal for bank’s intentions to securitize. Originality/value This study contributes to a better understanding of the reasons that urge banks to securitize. It also presents, using a Heckman selection procedure, a detailed analysis that discriminates between different types of securitization.

Linking bank regulatory capital buffer to business cycle fluctuations

2018 ◽  
Vol 45 (3) ◽  
pp. 565-585 ◽  
Author(s):  
Kolade Sunday Adesina ◽  
John Muteba Mwamba
Keyword(s):  
Panel Data ◽  
Market Power ◽  
Commercial Banks ◽  
Basel Iii ◽  
Regulatory Capital ◽  
Revenue Diversification ◽  
African Countries ◽  
Data Set ◽  
Content Type ◽  
Cyclical Behavior

Purpose The purpose of this paper is to assist bank regulators in Africa who are currently considering the implementation of Basel III countercyclical capital buffer (CCB) requirement. Design/methodology/approach Using a panel data set of 129 commercial banks operating in 14 African countries over the period 2004–2014, this paper estimates the system generalized method of moments regression to examine the impact of business cycle on banks’ regulatory capital buffers and attempts to identify the influence of bank revenue diversification, market power and cost of funding (CF) on bank regulatory capital buffers. It further carries out some robustness analyses using a panel data set of 257 commercial banks in 23 African countries over the period 2004–2014. Findings The results show that higher regulatory capital buffers are associated with higher market power, higher revenue diversification and higher CF. Additionally, the results show significant evidence of procyclical behavior of bank capital buffers (BUFs) in the sampled countries. Practical implications The results of this study suggest that African banking systems are not exposed to contagion and systemic risks arising from countercyclical movements of BUFs to the real economy. Therefore, this study does not support the implementation of the Basel III CCB requirement in the sampled African countries. Originality/value Considering that the results of existing studies on the cyclical behavior of BUFs are inconclusive, there is value in studying the cyclical movements of bank regulatory capital buffers in a set of countries that has not been analyzed before. Toward this direction, this is the first empirical study focusing on the cyclical behavior of bank regulatory capital buffers in Africa. Besides examining the cyclical behavior of bank regulatory capital buffers, this paper further investigates the effects of bank revenue diversification, market power and CF on bank regulatory capital buffers.

scholarly journals The impacts of financial regulations: solvency and liquidity in the post-crisis period

2017 ◽  
Vol 25 (3) ◽  
pp. 253-270 ◽  
Author(s):  
Colleen Baker ◽  
Christine Cummings ◽  
Julapa Jagtiani
Keyword(s):  
Regression Analysis ◽  
Financial Crisis ◽  
Capital Markets ◽  
Basel Iii ◽  
Regulatory Capital ◽  
Accounting Data ◽  
Content Type ◽  
Financial Regulations ◽  
Capital Ratios ◽  
Level 1

Purpose Basel III and the capital stress testing introduced new requirements and new definitions while retaining the structure of the pre-2010 requirements. The total number of requirements increased, making it difficult to determine which and how many constraints are binding. The purpose of this paper is to discuss the new financial regulations in the post-financial crisis period, focusing on the capital and liquidity regulations. Design/methodology/approach The authors explore the impact of financial regulations using various data sources – financial and accounting data from Y-9C Reports. Market data such as daily bond trading from TRACE through the Wharton Data Research Services and Treasury yield from the Bloomberg. The authors use regression analysis to examine the roles of capital adequacy and liquidity regulations. Findings The authors’ analysis in this paper suggest that Basel III, CET1 and Level 1 HQLAs requirements post-financial crisis have reshaped the balance sheets of large financial institutions, with some differential impacts on traditional versus capital markets banks. These changes appear to respond to the binding constraints (CET1 being a preponderance of required regulatory capital, Level 1 HQLAs a majority of required HQLAs and the expense of both) created by these new requirements, which also appear to have constrained asset growth at such institutions. Consistent with the authors’ view, their results suggest that the new requirements are less constraining for large traditional banks (such institutions show a rapid increase in CET1 capital to steady-state levels by 2012 and strong retail deposit rebuilding resulting in a relatively low required HQLA) and much more so, particularly the liquidity requirement, for the capital markets banks (such institutions show continuous building of CET1 capital over the post-crisis observation period, declines in the share of trading assets and increases in the share of HQLAs combined with efforts to increase retail deposits). Credit risk spreads rose dramatically during the financial crisis of 2008-2009. Although decreased, they remain higher and with greater dispersion (for both groups of banks) than pre-crisis. Preliminary regression analysis suggests that the market responds to changes in measured liquidity, rather than the regulatory capital ratios, when pricing bank risk (as reflected on bond spreads). Research limitations/implications The estimation is based on historical relationship in the data. We must be cautious in extrapolating the results in a different environment. Practical implications There appears to be an arbitrage between HQLA and retail deposits. Capital markets banks and traditional banks follow different business models as evident in the analysis in this paper. Social implications Market pricing suggests that the liquidity measures are more transparent and easier to understand. Capital ratios are not as easy to interpret. Originality/value Original research. To the authors’ knowledge, there is no paper that examines impacts of capital and liquidity regulations after the crisis at capital markets banks vs traditional banks – using both accounting data and market data.

scholarly journals REGULATORY CAPITAL MODELING FOR CREDIT RISK

2015 ◽  
Vol 18 (05) ◽  
pp. 1550034 ◽  
Author(s):  
MAREK RUTKOWSKI ◽  
SILVIO TARCA
Keyword(s):  
Credit Risk ◽  
Banking Sector ◽  
General Setting ◽  
Capital Adequacy ◽  
Dependence Structure ◽  
Regulatory Capital ◽  
Loss Distribution ◽  
Portfolio Loss Distribution ◽  
Portfolio Loss ◽  
Irb Approach

The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the banking sector against insolvency. We outline the mathematical foundations of regulatory capital for credit risk, and extend the model specification of the IRB approach to a more general setting than the usual Gaussian case. It rests on the proposition that quantiles of the distribution of conditional expectation of portfolio percentage loss may be substituted for quantiles of the portfolio loss distribution. We present a more compact proof of this proposition under weaker assumptions. Then, constructing a portfolio that is representative of credit exposures of the Australian banking sector, we measure the rate of convergence, in terms of number of obligors, of empirical loss distributions to the asymptotic (infinitely fine-grained) portfolio loss distribution. Moreover, we evaluate the sensitivity of credit risk capital to dependence structure as modeled by asset correlations and elliptical copulas. Access to internal bank data collected by the prudential regulator distinguishes our research from other empirical studies on the IRB approach.

What is good and bad with the regulation supporting the SME’s credit access

2020 ◽  
Vol 28 (4) ◽  
pp. 569-586 ◽  
Author(s):  
Pietro Vozzella ◽  
Giampaolo Gabbi
Keyword(s):  
Systematic Risk ◽  
Capital Requirements ◽  
Basel Iii ◽  
Bank Capital ◽  
Large Firms ◽  
Regulatory Capital ◽  
Firm Level ◽  
Data Set ◽  
Content Type ◽  
Asset Correlation

Purpose This analysis asks whether regulatory capital requirements capture differences in systematic risk for large firms and micro-, small- and medium-sized enterprises (MSMEs). The authors explore whether bank capital regulations intended to support SMEs’ access to borrowing are effective. The purpose of this paper is to find out whether the regulatory design (particularly the estimate of asset correlations) positively affects the lending process to small and medium enterprises, compared to large corporates. Design/methodology/approach The authors investigate the appropriateness of bank capital requirements considering default risk of loans to MSMEs and distortions in capital charges between MSMEs and large firms under the Basel III framework. The authors compiled firm-level data to capture the proportions of MSMEs and large firms in Italy during 2000–2014. The data set is drawn from financial reports of 708,041 firms over 15 years. Unlike most empirical studies that correlate assets and defaults, this study assesses a firm’s creditworthiness not by agency ratings or by sampling banks but by a specific model to estimate one-year probabilities of default. Findings The authors found that asset correlations increase with firms’ size and that large firms face considerably greater systematic risk than MSMEs. However, the empirical values are much lower than regulatory values. Moreover, when the authors focused on the MSME segment, systematic risk is rather stable and varies significantly with turnover. This analysis showed that the regulatory supporting factor represents a valuable attempt to treat MSME loans more fairly with respect to banks’ capital requirements. Basel III-internal ratings-based approach results show that when the supporting factor is applied, the Risk-Weighted-Assets (RWA) differences between MSMEs and large firms increase. Research limitations/implications The implications of this research is that banking regulators to make MSMEs support more effective should review asset correlation estimation criteria, refining the fitting with empirical evidence. Practical implications The asset correlation parameter stipulated by the Basel framework is invariant with economic cycles, decreases with borrowers’ probability of default and increases with borrowers’ assets. The authors found that those relations do not hold. This way, asset correlations fall below parameters defined by regulatory formula, and SMEs’ credit risk could be overstated, resulting in a capital crunch. Originality/value The original contribution of this paper is to demonstrate that the gap between empirical and regulatory capital charge remains high. When the authors examined the Basel III-IRBA, results showed that when the supporting factor is applied, the RWA differences between MSMEs and large firms increase. This is particularly strong for loans to small- and medium-sized companies. Correctly calibrating asset correlations associated with the supporting factor eliminates regulatory distortions, reducing the gap in capital charges between loans to large corporate and MSMEs.

Securitization and Italian banks’ risk during the crisis

2014 ◽  
Vol 15 (4) ◽  
pp. 458-478 ◽  
Author(s):  
Francesca Battaglia ◽  
Maria Mazzuca
Keyword(s):  
Financial Crisis ◽  
Credit Risk ◽  
Risk Profile ◽  
Ordered Probit ◽  
Empirical Literature ◽  
Basel Iii ◽  
Content Type ◽  
Probit Models ◽  
Different Types ◽  
Ordered Probit Models

Purpose – The purpose of this study was to examine the 2007-2009 financial crisis to analyze how securitization relates to the Italian bank risk profile, both in terms of credit and liquidity risks. Design/methodology/approach – To test our research hypotheses, we adopt ordered probit models, in which we regress the changes in credit risk and liquidity on a set of regressors, including two securitization dummy variables plus a vector of control variables. Findings – Our results show that the impact of securitization on the originators risk-taking is not uniform. When credit risk is considered, the securitization effects seem to be statistically significant only during the crisis period. However, when we turn to analyze the bank’s liquidity position, our results show that securitization improves it both during the pre-crisis and the crisis years. Our results support the Basel III initiatives aimed to realize a better integration between the different types of risks (i.e. credit and liquidity risks). Research limitations/implications – The major limitation of our study is related to the analyzed geographic area. Practical implications – First, our results support the Basel III initiatives aimed to realize a better integration between the different types of risks (i.e. credit and liquidity risks). In general, the broad policy implication of the paper is that in some contexts, such as the Italian market, securitization does not necessarily produce negative effects in terms of bank’s risk. Originality/value – This study contributes to the empirical literature on the effects of securitization for banks in several ways. First, we consider the complexity of the bank’s risk profile; second, despite the importance of the Italian securitization market, there is a research void on it. Furthermore, unlike previous studies, our analysis covers the period 2000-2009, including the financial crisis years. Finally, to our knowledge, our methodology (ordered probit models) has not been used in the past in this context.

Predicting credit risk on the basis of financial and non-financial variables and data mining

2018 ◽  
Vol 17 (3) ◽  
pp. 316-340 ◽  
Author(s):  
Sihem Khemakhem ◽  
Younes Boujelbene
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Risk ◽  
Decision Trees ◽  
Class Imbalance ◽  
Data Sets ◽  
Content Type ◽  
Financial Variables ◽  
Artificial Neural

PurposeData mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting parameters and the presence of unbalanced data sets is a very typical problem of this technique. This study aims to provide a new method for evaluating credit risk, taking into account not only financial and non-financial variables, but also the class imbalance.Design/methodology/approachThe most significant financial and non-financial variables were determined to build a credit scoring model and identify the creditworthiness of companies. Moreover, the Synthetic Minority Oversampling Technique was used to solve the problem of class imbalance and improve the performance of the classifier. The artificial neural networks and decision trees were designed to predict default risk.FindingsResults showed that profitability ratios, repayment capacity, solvency, duration of a credit report, guarantees, size of the company, loan number, ownership structure and the corporate banking relationship duration turned out to be the key factors in predicting default. Also, both algorithms were found to be highly sensitive to class imbalance. However, with balanced data, the decision trees displayed higher predictive accuracy for the assessment of credit risk than artificial neural networks.Originality/valueClassification results depend on the appropriateness of data characteristics and the appropriate analysis algorithm for data sets. The selection of financial and non-financial variables, as well as the resolution of class imbalance allows companies to assess their credit risk successfully.

scholarly journals A Critical Review Of The Basel Margin Of Conservatism Requirement In A Retail Credit Context

2017 ◽  
Vol 16 (4) ◽  
pp. 257-274 ◽  
Author(s):  
Riaan De Jongh ◽  
Tanja Verster ◽  
Elzabe Reynolds ◽  
Morne Joubert ◽  
Helgard Raubenheimer
Keyword(s):  
Risk Factor ◽  
Financial Institutions ◽  
Basel Ii ◽  
Probability Of Default ◽  
Regulatory Capital ◽  
Loss Given Default ◽  
Asymptotic Risk ◽  
Retail Credit ◽  
Complex Models ◽  
Irb Approach

The Basel II accord (2006) includes guidelines to financial institutions for the estimation of regulatory capital (RC) for retail credit risk. Under the advanced Internal Ratings Based (IRB) approach, the formula suggested for calculating RC is based on the Asymptotic Risk Factor (ASRF) model, which assumes that a borrower will default if the value of its assets were to fall below the value of its debts. The primary inputs needed in this formula are estimates of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Banks for whom usage of the advanced IRB approach have been approved usually obtain these estimates from complex models developed in-house. Basel II recognises that estimates of PDs, LGDs, and EADs are likely to involve unpredictable errors, and then states that, in order to avoid over-optimism, a bank must add to its estimates a margin of conservatism (MoC) that is related to the likely range of errors. Basel II also requires several other measures of conservatism that have to be incorporated. These conservatism requirements lead to confusion among banks and regulators as to what exactly is required as far as a margin of conservatism is concerned. In this paper, we discuss the ASRF model and its shortcomings, as well as Basel II conservatism requirements. We study the MoC concept and review possible approaches for its implementation. Our overall objective is to highlight certain issues regarding shortcomings inherent to a pervasively used model to bank practitioners and regulators and to potentially offer a less confusing interpretation of the MoC concept.

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