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This study aims to render a fundamental assessment of the Basel II internal ratings-based (IRB) approach by taking readings of the Australian banking sector since the implementation of Basel II and comparing them with signals from macroeconomic indicators, financial statistics and external credit ratings. The IRB approach to capital adequacy for credit risk, which implements an asymptotic single risk factor (ASRF) model, plays an important role in protecting the Australian banking sector against insolvency.

Realisations of the single systematic risk factor, interpreted as describing the prevailing state of the Australian economy, are recovered from the ASRF model and compared with macroeconomic indicators. Similarly, estimates of distance-to-default, reflecting the capacity of the Australian banking sector to absorb credit losses, are recovered from the ASRF model and compared with financial statistics and external credit ratings. With the implementation of Basel II preceding the time when the effect of the financial crisis of 2007-2009 was most acutely felt, the authors measure the impact of the crisis on the Australian banking sector.

Measurements from the ASRF model find general agreement with signals from macroeconomic indicators, financial statistics and external credit ratings. This leads to a favourable assessment of the ASRF model for the purposes of capital allocation, performance attribution and risk monitoring. The empirical analysis used in this paper reveals that the recent crisis imparted a mild stress on the Australian banking sector.

Given the range of economic conditions, from mild contraction to moderate expansion, experienced in Australia since the implementation of Basel II, the authors cannot attest to the validity of the model specification of the IRB approach for its intended purpose of solvency assessment.

Access to internal bank data collected by the prudential regulator distinguishes this paper from other empirical studies on the IRB approach and financial crisis of 2007-2009. The authors are not the first to attempt to measure the effects of the recent crisis, but they believe that they are the first to do so using regulatory data.

The purpose of this paper is to contribute to the debate on the current economic crisis and to highlight the importance of the counter‐party credit risk, which was surprisingly neglected by the Basel Committee on Banking Supervision in its proposed enhancements to the Basel II framework. The paper supports the proposition that there is an incentive for synergy between bank management, corporate management and auditors as long as all these parties' remuneration schemes are based on the same principles.

In this paper an advanced IRB corporate credit‐rating system is constructed in accordance with the Basel II framework and the current literature. The impact of common creative accounting and banking practices on the manipulation of that system is explored.

The paper shows how creative accounting and banking practices can be used in manipulating an advanced IRB corporate credit‐rating system, and thus presenting a high‐risk corporation as a highly attractive (low‐risk) bank customer.

The regulatory authorities should take into consideration the inability of rating agencies to ascertain the risk associated with the US sub‐prime mortgage market and the decline of auditors' independence.

The contribution of the paper is the propositions made to the Basel Committee on Banking Supervision in order to enhance the Basel II framework, and avoid repetition of the current economic crisis in the future.

I have been a regulator now for nine years. But for many years before that, in an explicitly non‐regulatory career at the Bank of England, I was very conscious of the efforts being made ‐ especially between the Bank and the Federal Reserve ‐ to get a dialogue under way in what bankers now know as the Basel Committee. Founders of that Committee, such as George Blunden, no doubt watched in a mixture of awe and horror at the subsequent growth of the animal they helped to create. When Basel 1 was being formulated in the mid‐1980s, there was, of course, a widespread recognition that there were many different reasons why a bank could fail. But there was a general and accurate perception that credit risk was the primary threat. An ‘adequate’ amount of capital and the division of assets into a relatively modest number of credit risk buckets were seen as a truly significant step forward. And as to what was ‘adequate’ capital, I am told that 8 per cent was arrived at as the highest number they dared to think they could get agreement on. It did not fall out (nor could it have fallen out) of any complicated mathematical formula or extensive historical research.

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.

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.

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.

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.

This paper provides a comprehensive overview of the gradual evolution of the supervisory policy adopted by the Basel Committee for the regulatory treatment of asset securitisation. The pathology of the new “securitisation framework” is carefully highlighted to facilitate a general understanding of what constitutes the current state of computing adequate capital requirements for securitised credit exposures. Although a simplified sensitivity analysis of the varying levels of capital charges depending on the security design of asset securitisation transactions is incorporated, the author does not engage in a profound analysis of the benefits and drawbacks implicated in the new securitisation framework.

This paper is a first attempt to empirically calibrate the default and asset correlation for large companies in India and elaborate its implications for credit risk capital estimation for a bank.

The authors estimate default probabilities and default correlations of long‐term bonds of 542 Indian corporates using rating transitions and pair‐wise migrations over ten year cohorts of firms. Further, the implicit asset correlation from the estimated default correlations and default thresholds are derived using the asymptotic single risk factor approach.

The authors find evidence that default correlations are time variant and vary across rating grades and industries. The highest correlations are observed between companies within the same rating grades (systematic risk impact) and within the same industry (industry specific impact). More interestingly, significantly smooth monotonic relationship between the probability of default (PD) and asset correlation as prescribed by the Basel II IRB document (2006) are not found. Moreover, it is found that the asset correlation range for Indian corporates do not match with what is prescribed for corporate exposures by BCBS.

The authors address the dilemma implied by the negative relationship between PD and asset correlation as suggested by BCBS IRB formula and other research for developed economies with estimates of asset correlation for and emerging market like India and demonstrate its implications on the estimation of credit risk capital.

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.

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.

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.

The credit VaR of a loan portfolio could be calculated from the bank easily, without the use of additional complicated algorithms and systems.

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.

This paper proposes a method that estimates credit ratings by mapping empirical probability of default (PD) and standardized historical financial ratios. Unlike standard approaches such as the parametric logit model. discriminant analysis. neural network. and survival function model. the proposed approach has an advantage of offering a multiple credit rating categories. as opposed to either default or not default. of obligors. It would provide an useful information to practitioners because the probability of default for each credit rating category is a critical input under New Basel Capital Accord. Emoirical results based upon the historical PD and financial ratios of Korean savings bank industry from 2000 and 2003 suggest that the industry’s average credit rating belong to a speculative grade. that is BB and below.

In addition, the computed transition matrix indicates that volatility of transition matrix fluctuates substantially each year and the orobability of staying in the same rating category at the end of the year tended to be much smaller than the average reported by the rating agencies for the overall Korean companies. The proposed method can easily be applied to industries other than savings bank industry.

Estimation of default and asset correlation is crucial for banks to manage and measure portfolio credit risk. The purpose of this paper is to find empirical relationship between the default and asset correlation with default probability, to understand the effect of systematic risk.

The authors have estimated single default and implicit asset correlations for banks and corporates in India and compare it with global scenario. This paper deduces a simple methodology to estimate the default correlations from the variance of temporal default rates. Next, the asset correlations have been estimated analytically by decomposition of variance equation in Merton's one factor risk model following approaches of Gordy and of Bluhm and Overbeck.

The authors empirically find a negative relationship between asset correlation and the probability of default using Moody's global corporate data that support Basel II internal ratings‐based (IRB) correlation prescription. However, they do not find any smooth relationship between the probability of default (PD) and asset correlation for Indian corporate. The magnitude of correlation estimates based on a large bank's internal rating‐wise default rates are much lower than what is prescribed by the Basel committee. Thus, the standardized correlation figures as assumed by the Basel Committee on Banking Supervision need to be properly calibrated by the local regulators before prescribing their banks to calculate IRB risk weighted assets.

These correlation estimates will help the regulators, insurance firms and banks to understand the linkage between counterparty default risks with the systematic factors. The findings of this paper could be used further in estimating portfolio economic capital for large corporate exposures of banks and insurance companies.

A credit risk model suitable for agricultural lenders is identified. The model incorporates sector correlations and is applied to the loan portfolio of an agricultural credit association to create a distribution of loan losses. The distribution is used to derive the lender’s expected and unexpected losses. Results of the analysis indicate that the association is more than adequately capitalized based on 1997S2002 data. Since the capital position of the association is lower than that of most other associations in the Farm Credit System, this raises the issue of overcapitalization in the System.