Basel II - Standardised Approach for Credit Risk

This approach allows banks to measure credit risk in a standardized manner based on external credit assessments. Rating agencies try to capture risk sensitivity using ratings. The risk weights are inversely related to the rating of the counter party. A higher rating indicates lower risk. National supervisors ensure the External Credit Assessment Institution (ECAI) meet the criteria set. The criteria are:

  1. Objectivity
  2. Independence
  3. International Access
  4. Transparency
  5. Disclosures
  6. Resources
  7. Credibility

To credit rate sovereigns the country scores of Export Credit Agency are recognized.

In the case of commercial agencies two options are available. All banks under a certain country are designated a risk weight one notch below the risk weight assigned to it’s sovereign. But for countries with a credit rating of BB+ OR B- and banks of unrated countries the weights are given at 100%.

Alternatively an external credit assessment by the bank is used for its risk weights. In this case unrated banks have a risk weight of 50%.

The claims of sovereigns and their central banks are assigned risk weights as follows:

Credit Rating of SovereignsRisk Weight of SovereignsRisk Weight for banks in that country
AAA to AA-
A+ to  A-
BBB+ to BBB-
BB+ to B-
Below B-
Unrated
0%
20%
50%
100%
150%
100%
20%
50%
100%
100%
150%
100%

Keeping in line with inverse risk weights and risk ratings, you will observe that weaker sovereigns or banks have risk weights far above 20%. This is unlike the earlier Basel I Accord where all sovereigns enjoyed a risk weight of 0% and banks had a uniform risk weights 20%.

In the case of corporates, claims have risk weights based on credit ratings similar to the ones given to banks in the table. The risk weights for unwanted claims are 100% .Unrated corporates cannot have risk weights lower than their sovereigns of incorporation. The supervisor should increase the standard risk weight of unrated claims when they judge that a higher risk is warranted based on default experience.

The standardized approach gives a risk weight of 75% to retail and SME exposures. The four eligibility criteria for inclusion in to the retail category are:

  1. Exposure is to an individual person or persons or to a small business.
  2. Exposure is the form of revolving credit, personal term loans, small business facilities. Securities, bonds and equities are specifically excluded.
  3. The portfolio must be sufficiently diversified-this is called Granularity criterion. One may set a numerical limit that no aggregate exposure to one counterpart can exceed 0.2% of the overall retail portfolio
  4. Low value of individual exposures. The maximum aggregated retail exposure to a     single counterparty cannot exceed an absolute threshold of Euro 1 million.

In the case of lending fully secured by mortgage on residential property, the lending will have a risk weight of 35%. Thus all housing loan property may qualify for a risk weight of 35%. A fully secured loan on commercial property has a risk weight of 100%.

Past due loans attract a risk weight of 150% when specific provisions are less than 20% of the loan amount. In the case of higher provisioning the risk weight would be 100%.

Past due housing loans attract a risk weight of 100%, if the provisioning is below 20%. When higher provisioning is available the risk weight would be 50%.

In case of off-balance sheet items, credit conversion factor (CCF) is used like the ones in the Basel I accord.

A bank is allowed netting of the security from the outstanding exposure provided it has the right to liquidate the security promptly in the event of default. Thus liquid securities, such as, cash margins, term deposits, assigned insurance policies, duly pledged/transferred shares, debentures, bonds etc could be available for netting to the extent of reusable value provided the security can be promptly liquidated.

In cases where the claim amount is guaranteed by another party or agency, which has better rating, the risk weight would reduce correspondingly. For example, if unrated exposure is guaranteed by AA corporate, the risk weight will be 20%, corresponding to the AA rating.

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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

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.