Basel II – Capital Charge for Credit Risk

Credit risk is defined as the possibility of losses associated with reduction of credit quality of borrowers or counterparties. In a bank’s portfolio, losses arise from outright default due to inability or unwillingness of a customer or counterparty to meet commitments in relation to lending, trading settlements, or any other financial transaction. Alternatively, losses occur from reduction in portfolio value due to deterioration in credit quality.

Under Basel I framework, assets were assigned uniform risk weights based on their category. For example, exposures to sovereigns were assigned a risk weight of 0%. Claims against banks were given a risk weight of 20%. Advances to corporates, individuals and firms were assigned 100% risk weight. The rating or health of the counterparty was not taken into account. Exposures to some of the banks can be riskier than exposures to some of the corporates. However, all banks enjoyed the risk weight of 20% and all corporates had a 100% risk weight.

This situation was corrected under Basel II. Under the revised accord, along with the category of a customer, his credit rating is given due importance.

Banks are permitted a choice between two broad methodologies for calculating their capital requirements for credit risk. These are:

Standardized Approach

Under this approach, credit risk is measured in a standardized manner based on external credit rating assessment.

Internal rating Based (IRB Approach)

Under this approach, banks are allowed to use their internal rating system for credit risk. This will be subject to the explicit approval of the bank’s supervisor. This approach has two options:

  1. Foundation IRB Approach
  2. Advanced IRB Approach

In the coming posts, we will discuss each of the above methods in detail.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.