Delayed Completion of Basel 3 Reform Is Credit Negative for Banks

According to Moody’s the 3rd January announcement from the Basel Committee on Banking Supervision (BCBS) that the final decision on the completion of the Basel 3 reform (also referred to as Basel 4) has been postponed, is credit negative for Banks. The delay could also dent investors’ confidence in banks’ capital ratios and result in higher cost of funding.

The scheduled January meeting of the Group of Central Bank Governors and Heads of Supervision (GHOS) to agree on final capital regulations was postponed because of the lack of agreement on calibrating parameters for the use of internal capital models. The protracted process is credit negative for banks and signals the supervisors’ difficult reach for a consensus on adopting rules for a common/global capital framework, which is critical to preserve a level playing field.

The delay could also dent investors’ confidence in banks’ capital ratios and result in higher cost of funding.

GHOS’ decision to postpone the meeting is unsurprising given differing views among BCBS members. The BCBS is striving to define a revised framework that fairly reflects risks and does not result in “significant” capital increases. Officials from EU countries including France and Germany and the European Commission are worried about choking off bank lending to economies where loans are the primary form of corporate finance. Although what would constitute a significant capital increase has not been quantified, BCBS Chairman Stefan Ingves last month acknowledged that this objective does not mean avoiding any increase for any bank and it may result in significant increases at some banks.

BCBS members agree on the overarching objective, which is to restore confidence in banks’ calculation of their risk-weighted assets (RWAs). That means achieving greater consistency and reducing variability in the calculation methodology for RWAs. The BCBS seeks to constrain the benefit of modelling techniques: in some cases, supervisors consider that the models too frequently result in low capital requirements and the BCBS is specifically targeting banks that have developed aggressive modelling techniques. However, defining the threshold at which such capital benefits become unacceptable is proving thorny for BCBS.

BCBS has a consensus on the need to get rid of unjustified variability, yet lacks a consensus on the acceptable level of difference between the “standardized” measure of risks and banks’ internal model estimates. Banks will be required to assess their risks under both methods and the general floor, which will be set between 60% and 90% of standardized risk weights, will determine the benefits risk modelling could bring: the lower the floor, the greater banks can benefit from models’ outcomes. Those regulators who place greater trust in banks’ internal models favor a lower floor while others, based on well-documented failures that occurred during the financial crisis, argue the standardized approach should drive the outcome, and therefore prefer a higher floor. The more intensive use of models by EU banks makes them sensitive to the floor.

The final decision on the general floor will attract a lot of attention from investors. If the general floor is set at a high level (close to 90%), the GHOS will have been relatively conservative; even more so if the implementation period is short. Were the floor to be set closer to 60% with a long transition phase, it would indicate a more permissive stance. However there are also many technical parameters that are critical to form a view on the framework’s toughness (or lack thereof); for example, floors could be imposed at the model level (setting a minimum level of probability of default or loss given default).

For now, the absence of an agreement and the BCBS’ difficulties in clinching a deal continue to fuel investors’ lack of confidence in RWAs and hence in capital ratios and skepticism towards the adoption of harmonized rules.

Author: Martin North

Martin North is the Principal of Digital Finance Analytics

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