Paper Example on Banking Supervision

Defining PD, LGD, EAD and CCF Parameters

The probability of Default (PD) - in the financial context, PD refers to the likelihood that a borrower will not meet his debt obligations over a particular period. PD generally describes the creditworthiness as well as the risk management estimates which apply to the banks assigned to same risk exposures (Schneider et al., 2017).

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Loss Given Default (LGD) - LGD refers to the shared asset (amount of money) that a banking institution loses when a borrower fails to meet the debt obligation/defaults on a loan. In asset appropriation, computations for the LGD is determined upon a thorough review of the bank's portfolio, mostly by the use of same risk exposures and cumulative losses.

Exposure at Default (EAD) - refers to the predicted asset loss assigned to same risk exposures to a banking institution when a borrower defaults on a particular loan. This parameter is likewise used to compute the credit risk capital of banking institutions under the economic money Basel II (McKinsey, 2016).

Credit Conversion Factor (CCF) - refers to a parameter used to determine the exposure at a default of the potential possibility that a borrower of a bank loan is likely to fail to meet his/her debt obligation.

The above parameters are important measures that most banking institutions use to define the exposures associated with cash defaults. Based on the study, the Basel Committee on Banking Supervision (BCBS) highlights some risk of significant capital shortfalls that many European banks are exposed to. The BCBS study found out that there is a notable dispersion in the relative riskiness of obligators, revealing a high correlation in how banking institutions rank a portfolio of individual borrowers. Based on these findings, the BCBS has provided a comprehensive perspective and assessment of the relative riskiness while providing recommendations on the approach the banking institutions can take (Schneider et al., 2017).

Changes Proposed by the Basel Committee on Banking Supervision (BCBS)

The aspect of large corporate entities and other banking institutions being considered to be low-default exposures has made the probability of default (PD), loss given default (LGD), exposure at default (EAD) and Credit conversion factor (CCF) parameters estimation challenging to be implemented (Schneider, et al., 2017). This has been proven so because the internal estimates of potential defaults of most banks show massive losses from such exposures. More so, most credit rating agencies tend rating such disclosures highly thereby making them a subject of significant market analysis (McKinsey, 2016). The need to put in place standardised approaches to help estimate the losses based on the available market data and solve the issue of credit risk is required. For this purpose, the Basel Committee on Banking Supervision (BCBS) has proposed some changes to help remove the rigidity in IRB approach for large corporate entities and bank exposures (Schneider et al., 2017). The proposals, however, have wide implications on the methods used by most corporate agencies in which they are typically insufficient information to estimating the PD, LGD, EAD and CCF parameters more reliably. The various proposals were;

Exposures related with financial corporations such as banks and insurance entities will be subject to standardised approach. Vulnerabilities related to corporate entities that have assets exceeding fifty billion Euros will also be subjected to the graded approach. Exposures associated with corporate entities that have annual revenues greater than or equal to two hundred million Euros and assets less than or equal to fifty billion Euros will be eligible for the IRB foundation approach.

Exposures related with corporate entities that have annual revenues less than or equal to two hundred million Euros and assets less than or equal to fifty billion Euros will be eligible for the AIRB foundation approach.

However, the above-proposed approaches are different from the ones in the current Basel III agreements in the sense that the proposed one helps to ensure objectivity and consistency in mitigating potential cliff effects. Additionally, the proposed approaches help to provide alternative measures for consolidating the financial data of banking institutions in such a manner that measured amounts can be updated after every three fiscal years. Furthermore, the changes help to belong corporate entities to reflect their expectations in putting in place reliable data estimates of the model parameters of PD and LGD as standardised approaches (McKinsey, 2016).

Potential Impact of Parameter Change on Banks

When exposures related with financial corporations such as banks and insurance entities are subjected to standardised approach, banks are bound to make positive strides in the management of their finances. On the other hand, if exposures related with corporate entities that have annual revenues greater than or equal to two hundred million Euros and assets less than or equal to fifty billion Euros will be eligible for the IRB foundation approach, sanity will be restored in the determination of eligible institutions. Using this proposal as a mechanism of change, banking institutions and corporate entities are likely to ensure better services in the regulatory capital floors. Furthermore, if exposures related with corporate entities that have assets exceeding fifty billion Euros will also be subjected to the standardised approach, the likelihood of loss of funds will be lowered given the measures put in place to help mitigate the issues (McKinsey, 2016).

The proposals will, however, persuasively set a lower bound for the estimators in internal model approaches of the regulatory capital floors. These proposals, likewise suggest that European banks must reduce variations in credit risk-weighted assets as a means of mitigating their impact on capital floors (McKinsey, 2016). In line with these proposals, BCBS has additionally provided an in-depth analysis of the capital floors using a number of model parameters which are PD, LGD, EAD and CCF which are chief determinants to banks' exposure to default of the potential possibility that a borrower of a bank loan is likely to fail to meet his/her debt obligation (MASSEBACK, 2014). Therefore with these proposals in place, European banks mostly in Sweden, Belgium, Netherlands and Denmark will be highly affected because their key drivers are IRB output floors that potentially affect the mortgage-lending and corporate portfolios of banking institutions.

Analysis and Conclusion

In relative to banks' suitability to operate, the greatest impact of the proposal by the BCBS indicate that the IRB models are likely going to cause low losses in mortgage-lending and corporate portfolios. This implies that the relative losses will be given on estimated defaults that will not require more regulatory capital to be set. For this reason, I highly find the proposals not only relevant to the banking issues but also suitable to be adopted and implemented to help lower the increasing risk weights for banks' sovereign exposures.

Bibliography

MASSEBACK, S. I., 2014. A comparison of the IRB approach and the Standard Approach under CRR for purchased defaulted retail exposures. Master of Science Thesis Stockholm, Sweden 2014, May, pp. 3-8.

McKinsey, 2016. Basel Committee on Banking Supervision Consultative Document: Reducing Variation in Credit risk-weighted Aseets. Bank for International Settlement, 24 June, pp. 2-9.

McKinsey, 2016. Basel Committee on Banking Supervision: Frequently asked questions on Basel III monitoring ad hoc Exercise. Bank for International Settlement, 6 July , pp. 8-9.

Schneider, et al., 2017. Basel "IV": What's Next for Banks? Implications of Intermediate Results of new Regulatory Rules for European Banks. McKinsey&Company Global Risk Practice 2017, June, pp. 5-9.

Young, K.L., 2012. Transnational regulatory capture? An empirical examination of the transnational lobbying of the Basel Committee on Banking Supervision. Review of International Political Economy, 19(4), pp.663-688.