Basel Committee On Banking Supervision

TLDR: The Basel Committee's organizational structure is fluid as mentioned in this paper and is designed to facilitate consensus building to enhance understanding of key supervisory issues and to improve the quality of banking supervision worldwide.

Abstract: The Basel Committee developed as a cross-country regulatory response to the globalization of financial institutions and markets, which would otherwise connect national safety nets in an uncontrolled way. Its main objectives are to enhance understanding of key supervisory issues and to improve the quality of banking supervision worldwide. The membership of the Basel Committee is limited to the G10 member countries with Luxembourg, Belgium, and Switzerland. The Basel Committee's organizational structure is fluid. The procedures are designed to facilitate consensus building. The regular and confidential exchange of views, identification of best practices, and drafting of policy papers helps the authorities to find common ground without the distractions of external pressure. The Basel Committee has established itself as one of the central organs in the governance of global banking and finance. A core goal of the Basel Committee has always been to provide a forum for dialogue and information exchange. Keywords: banking supervision; Basel Committee's organizational structure; financial institutions

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Table 1: Risk Measures

Table 2: Comparison of risk aggregation methodologies

Full text

Basel Committee
on Banking Supervision
Range of practices and
issues in economic capital
frameworks
March 2009

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ISBN print: 92-9131-777-2
ISBN web: 92-9197-777-2

Table of Contents
Executive Summary..................................................................................................................1
Recommendations......................................................................................................................6
I. Introduction......................................................................................................................8
II. Use of economic capital measures and governance.......................................................9
A. Business-level use ...............................................................................................10
B. Enterprise-wide or group-level use ......................................................................12
C. Governance..........................................................................................................14
D. Supervisory concerns relating to use of economic capital and governance ........16
III. Risk measures...............................................................................................................19
A. Desirable characteristics of risk measures...........................................................19
B. Types of risk measures........................................................................................20
C. Calculation of risk measures................................................................................22
D. Supervisory concerns relating to risk measures ..................................................23
IV. Risk aggregation ...........................................................................................................24
A. Aggregation framework........................................................................................24
B. Aggregation methodologies .................................................................................25
C. Range of practices in the choice of aggregation methodology ............................30
D. Supervisory concerns relating to risk aggregation ...............................................31
V. Validation of internal economic capital models .............................................................32
A. What validation processes are in use? ................................................................34
B. What aspects of models does validation cover?..................................................37
C. Supervisory concerns relating to validation..........................................................38
Annex 1: Dependency modelling in credit risk models ...........................................................39
Annex 2: Counterparty credit risk ...........................................................................................46
Annex 3: Interest rate risk in the banking book.......................................................................53
Annex 4: Members of the Risk Management and Modelling Group .......................................63
Range of practices and issues in economic capital frameworks

Frequently asked questions

Q1. What are some of the elements that banks use to cover losses at higher levels of confidence?

In order to cover losses at higher levels of confidence, some banks consider capital instruments that may be loss-absorbing, more innovative or uncertain forms of capital such as subordinated debt. 

Q2. What are the major communication channels where economic capital measures could be used?

The major external communication channels where economic capital measures could be used include disclosure (eg annual reports, presentation materials for investors), dialogue with supervisory authorities and dialogue with rating agencies. 

Q3. What are the main reasons for the lack of flexibility of counterparty credit risk systems?

Fragmented computer systems and IT infrastructures, frequently driven by a variety of legacy infrastructures from merger and acquisition activity, are frequently cited culprits to the limitations associated with counterparty credit risk systems’ lack of flexibility. 

Q4. What is the role of the prepayment/non-maturity deposit modelling?

The prepayment/non-maturity deposit modelling may be carried out at local business level, to generate sensitivity to rate shocks at various stress levels, producing different prepayment/customer retention forecasts across interest rate shocks. 

Q5. What are the properties that can be assessed using a powerful tool?

The properties that could be assessed using a powerful tool and hence that are capable of robust assessment include: integrity of implementation; grounded in historical experience; risk sensitivity; sensitivity to the external environment; good marginal properties; rank ordering; and relative quantification. 

Q6. What are the main areas of improvement in the validation of models?

The main areas of improvement are in benchmarking of model parameters and the conduct of cross-firm comparisons of models, typified by the IACPM and ISDA study (2006) on portfolio credit risk models. 

Q7. What is the way to estimate correlations for other exposures?

Assuming that banks gather enough data to estimate more reliable correlations using internal data in the future, it would be useful for the industry to make progress in estimating correlations for other exposures, such as SME, retail, and structured products, and to analyse which data, models, and techniques are the most relevant for these portfolios. 

Q8. What are some of the common risks viewed by banks as better covered by ensuring internal?

Some risks are viewed by banks as better covered by ensuring internal control procedures are in order to mitigate risk and/or prepare contingency funding plans (eg liquidity risk). 

Q9. What are the risks related to counterparty risk that are particularly difficult to quantify?

Operational risks related to counterparty risk that are particularly difficult to quantify involve risks of new or rapidly growing businesses, risks in new products or processes, risks in intraday extensions of credit which are not properly captured in systems designed for end-of-day exposure capture, and risks in areas where there have been few historical instances of losses but where potential “tail events” may have severe consequences. 

Q10. What are the possible developments for the risk of interest rate shocks?

Among the possible developments are: (i) scenarios based on historical distributions; (ii) scenarios based on principal component (PC) decomposition of the yield curve; (iii) scenarios based on the GARCH models; (iv) scenarios based on options; (v) scenarios based on macroeconomic factors; and (vi) scenarios linking credit and interest rate risk. 

Q11. What is the main reason for the difficulty of the validation task?

It is recognised that this validation task is intrinsically difficult since it will typically require evaluation of high quantiles of loss distributions over long periods combined with data scarcity coupled with technical difficulties such as tail estimation. 

Q12. What are the main advantages of the summation and fixed diversification methodologies?

Although the most restrictive of the alternative methodologies, the main advantages of the summation and fixed diversification methodologies are simplicity in terms of data and computational requirements, and ease of communication about the method and interpretation of the outcome. 

Q13. What are some of the measures that banks use to plan for future events?

That is, banks place more emphasis on qualitative rather than quantitative tools and expect to rely on management actions to deal with future events. 

Q14. What is the link between a bank’s target rating and the choice of confidence level?

The link between a bank’s target rating and the choice of confidence level may be interpreted as the amount of economic capital that must be exceeded by available capital resources to prevent the bank from eroding its capital buffer at a given confidence level. 

Q15. Why do banks use a Monte Carlo approach to measure counterparty credit risk?

Due to the challenges of developing a highly nuanced view of counterparty credit risk for economic capital purposes, banks have developed ancillary processes to help manage and measure these risks. 

Q16. how much of the difference in economic capital estimates is attributable to correlation assumptions?

in mark-to-market mode, where changes in revaluations at the horizon for non-defaulted assets may also be correlated, and where the impact of differences in the modelling of correlations is larger, roughly a quarter of the observed difference in economic capital estimates is attributable to correlation assumptions.