Advanced Internal Ratings Based Approach Research Articles

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.

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

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.

Cross organizational standardized business process integration and its application in Saudi Arabian banking system regulation: A case study of advanced IRB regulation

The idea for this paper came after the recent financial crisis, its global consequences and specifically how it affected the banking sector. Financial institutions and regulators are – from a technical point of view - not fully integrated and automated yet. The inaccuracy in banks’ data and the long set interval period, quarterly, to send the information to the regulators leads to delays interventions by local supervisory regulators. Most of the banks are using an Internal Ratings Based (IRB) approach that allows them to use their own methods to calculate the credit risks, which makes it difficult for the regulators to verify and validate the banks’ data without adopting fully automated connectivity for the regulatory reporting system through sophisticated tools. The importance of this issue, for the central banks as well as the global economy, encourages us to investigate and to find solutions for the problem at hand. This paper is focused on the Advanced Internal Ratings Based (A-IRB) approach to evaluate credit risk due to the importance and the sensitivity of this approach on the banking sector. The flexibility of the A-IRB approach allow banks to use their own method to calculate the credit risk without regulators having the right tools to validate the data is a major issue . The second issue with the A-IRB approach is that the report is only delivered quarterly to the regulator (SAMA). This period is too long as decisions can be taken based on data that is almost a quarter old. Therefore, evaluating the existing framework and solving the issues concurrently is essential to improve the regulatory reporting system. To examine the situation of the regulatory reporting system, first, we reviewed literature on the Basel II&III regulations and the financial crisis, including impacts and responses. The second, we reviewed factors impeding the implementation of Basel II&III, including solutions to increase coordination and integration and a holistic approach that can mitigate the outstanding issues for the Regulatory Reporting System for the local banks in Saudi Arabia. We also evaluated the current system and the proposed system in two workshops and found that the proposed system showed considerable improvements of more than 100% in some areas and hence should be implemented. Our result is a framework solution that integrates a private cloud computing network with automated and integrated features such as a Business Process Manager, a Knowledge Management Engine, a SLA Lifecycle Manager, a Business Rules Engine and a Data Quality Regulator and the Enterprise Service Bus for communication and integration between the banks and SAMA. Keywords: Business Process, Business Rules, Bank Management

Exposure at default models with and without the credit conversion factor

The Basel II and III Accords allow banks to calculate regulatory capital using their own internally developed models under the advanced internal ratings-based approach (AIRB). The Exposure at Default (EAD) is a core parameter modelled for revolving credit facilities with variable exposure. The credit conversion factor (CCF), the proportion of the current undrawn amount that will be drawn down at time of default, is used to calculate the EAD and poses modelling challenges with its bimodal distribution bounded between zero and one. There has been debate on the suitability of the CCF for EAD modelling. We explore alternative EAD models which ignore the CCF formulation and target the EAD distribution directly. We propose a mixture model with the zero-adjusted gamma distribution and compare its performance to three variants of CCF models and a utilization change model which are used in industry and academia. Additionally, we assess credit usage – the percentage of the committed amount that has been currently drawn – as a segmentation criterion to combine direct EAD and CCF models. The models are applied to a dataset from a credit card portfolio of a UK bank. The performance of these models is compared using cross-validation on a series of measures. We find the zero-adjusted gamma model to be more accurate in calibration than the benchmark models and that segmented approaches offer further performance improvements. These results indicate direct EAD models without the CCF formulation can be an alternative to CCF based models or that both can be combined.

Do banks’ internal Basel risk estimates reflect risk?

Using supervisory data for U.S. banks, we evaluate the alignment of Basel II/III AIRB (advanced internal ratings based) risk estimates with portfolio risk. We use loan performance as a direct measure of portfolio risk as well as less direct market-based measures. Our results document that AIRB risk weights are highly correlated with loan performance and that, in contrast, Basel I risk weights are not. We find that capital requirements under the AIRB approach are higher than those under Basel I especially for portfolios recently under stress such as mortgages and some sovereign exposures. The alignment of Basel risk estimates with market-based risk indicators is less robust, although the association is nevertheless stronger under the AIRB approach. Our results support the view that internally generated risk weights are determined mostly by portfolio risk and, as a result, are substantially more risk sensitive than the fixed asset class based risk weights of Basel I.

Are Basel II Risk Weights Aligned with Risk?

Using supervisory data for US banks, we evaluate the alignment of Basel II/III AIRB (Advanced Internal Ratings Based) risk estimates with portfolio risk. We use loan performance as a direct measure of portfolio risk as well as less direct market-based measures. Our results document that loan performance is highly correlated with AIRB risk weights and that, in contrast, Basel I risk weights are not reflective of loan performance. We find that capital requirements under the AIRB approach are higher than those under Basel I especially for portfolios recently under stress such as mortgages and some sovereign exposures. The alignment of Basel risk estimates with market-based risk indicators is less robust although, the association is nevertheless stronger for AIRB risk weights relative to Basel I. Our results support the view that internally generated risk weights are determined mostly by portfolio risk and are, as a result, substantially more risk sensitive then the fixed asset type based risk weights of Basel I.

The calculation of capital requirement using Extreme Value Theory

The Basel Committee has suggested some formulas for calculating capital requirement using the Advanced Internal Ratings-Based Approach. However, these formulas were derived under the assumption of a normal distribution. Thus, the capital requirement estimated by the Basel formula may be incorrect when the asset distributions are not normal. Using an analysis of qualifying revolving retail exposures as an example, this paper introduces a formula based on the Extreme Value Theory to calculate the capital requirement. This formula is more general and accurate than its predecessors, because it can be used with any type of distribution. Numerical examples are provided to demonstrate that the capital requirement estimated by the Basel formula is less than by our formula when the asset distribution has a heavy tail, and more than by our formula when the distribution has a short tail. Our formula is also more sensitive to risk than competing models in the context of the recent financial crisis.

Marking-to-Model Credit and Operational Risks of Loan Commitments: A Basel-2 Advanced Internal Ratings-Based Approach

Marking-to-Model Credit and Operational Risks of Loan Commitments: A Basel-2 Advanced Internal Ratings-Based Approach

What do We Know about Loss Given Default?

The New Basel Accord will allow internationally active banking organizations to calculate their credit risk capital requirements using an internal ratings based (IRB) approach, subject to supervisory review. One of the modeling components is loss given default (LGD), the credit loss incurred if an obligor of the bank defaults. The flexibility to determine LGD values tailored to a bank's portfolio will likely be a motivation for a bank to want to move from the foundation to the advanced IRB approach. The appropriate degree of flexibility depends, of course, on a bank knows about LGD broadly and about differentiated LGDs in particular; consequently supervisors must be able to evaluate what a bank knows. The key issues around LGD are: 1) What does LGD mean and is its role in IRB? 2) How is LGD defined and measured? 3) What drives differences in LGD? 4) What approaches can be taken to model or estimate LGD? By surveying the academic and practitioner literature, with supportive examples and illustrations from public data sources, this paper is designed to provides basic answers to these questions. The factors which drive significant differences in LGD include place in the capital structure, presence and quality of collateral, industry and timing of the business cycle.