Improving regulatory capital allocation: a case for the internal ratings-based approach for retail credit risk exposures
| Published date | 28 May 2021 |
| Date | 28 May 2021 |
| Pages | 317-335 |
| DOI | https://doi.org/10.1108/JFRC-08-2020-0076 |
| Subject Matter | Accounting & finance,Financial risk/company failure,Financial compliance/regulation |
| Author | Robert Stewart |
Improving regulatory capital
allocation: a case for the internal
ratings-based approach for retail
credit risk exposures
Robert Stewart
Sir Arthur Lewis Institute of Social and Economic Studies,
University of the West Indies, St Augustine, Trinidad and Tobago
Abstract
Purpose –The purpose of this study is to demonstrate that the internal ratings-based (IRB) approach
provides more effective risk discrimination than the standardized approach when calculating regulatory
capitalfor retail credit risk exposures.
Design/methodology/approach –The author uses four retail credit data sets to compare regulatory
capital appropriation using the IRB approach and the standardized approach. The author follows the
regulatory capital calculationmethod recommended under Basel III. For the IRB approach, the authoruses a
logisticregression to determine the probability of default.
Findings –The results suggest that the IRB approach provides more effective riskdiscrimination across
individual exposures, which allows more regulatory capital to be held against riskier exposures and less
regulatory capitalto be held against less risky exposures.The author further argues that the Basel III output
floor, as presentlyconstructed, may disincentivize the use of the IRB approachand further diminish the value
of secured lendingunder the IRB approach. To address this issue, the author offers two simple adjustmentsto
the current designof the output floor.
Originality/value –While studies have argued the idea of risk-sensitive regulatory capital, the author has not
observed any research that empirically compares the risk-sensitivity of regulatory capital across retail credit
exposures, which makes up a significant portion of many banks’credit exposures. This study also highlights what
appears to be a major point of concern for the output floor, which is set to be phased in starting January 2022. This
is of particular value because this point has not appeared to receive anya ttentionin the literature thus far.
Keywords Basel III, Logistic regression, Probability of default, Regulatory capital, Exposure at Default,
Internal ratings-based approach, Standardized approach, Loss given default, Capital output floor
Paper type Research paper
1. Introduction
Under full implementation of Basel III’s Pillar I recommendations, banks could be required
to increase minimum regulatory capital from 8% to as much as a 13% minimum (Basel
Committee on Banking Supervision, 2010). Together with the minimum liquidity
requirement also introduced by the Basel Committee on Banking Supervision (2010), banks
will now face greater capital constraints on their balance sheets, and this has created
concerns for credit extensionand economic growth.
JEL classification –G21, G28
This research did not receive any specific grant from funding agencies in the public, commercial, or
not-for-profit sectors.
Declaration of interests: None.
Improving
regulatory
capital
allocation
317
Received17 August 2020
Revised11 January 2021
Accepted12 February 2021
Journalof Financial Regulation
andCompliance
Vol.29 No. 3, 2021
pp. 317-335
© Emerald Publishing Limited
1358-1988
DOI 10.1108/JFRC-08-2020-0076
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1358-1988.htm
Traditionally,the financial developmentliterature has indicated that credit extension has
a positive effect on economicgrowth, via the accumulation and allocation of financial capital
(King and Levine, 1993;Rajan and Zingales, 1998), and authors have argued that increased
capital regulation may constraincredit availability (Naceur et al.,2018;Roulet, 2018). On the
contrary, other authors have shown that regulatory capital increases could improve loan
quality (Boudriga et al.,2009;Kopecky and VanHoose, 2006) and even increase banking
efficiencies (Bitar et al., 2018;Kwanand Eisenbeis, 1997;Pasiouras et al., 2009). I delve into
this debate by arguing that if regulatorycapital is administered optimally, then increases in
regulatory capital can indeed provide improvements in loan quality and banking
efficiencies, whichmay benefit sustainable economic growth.
Evidence shows that the level of regulatory capital recommended under the Basel III
accord is not excessive but is actually within the levels required to absorb losses from
financial crises (Dagher et al., 2020) and promote economic growth (Martynova, 2015). I
argue then that the level of regulatory capital is not the issue, but rather the way in which
said capital regulationis administered.
Under the Basel III framework, banks have two general methods for calculating
regulatory capital(for credit risk):
(1) the standardized approach, which prescribes specific risk weights for asset classes;
and
(2) the internal ratings-based (IRB) approach, which allows banks to use internal
models to calculate probability of defaults, which are then used to compute risk
weights.
The IRB approach is further divided into the foundational IRB (F-IRB) and advanced IRB
(A-IRB) approaches. The F-IRB approach prescribes other parameters for calculating risk
weights (e.g. loss given default (LGD) and exposure at default (EAD)), while the A-IRB
approach allows banks to also estimate the referenced parameters usinginternal models. In
this study, I simplifymy investigation by focusing on the F-IRB approach.
Under the standardized approach, risk weightsare prescribed for different asset classes,
and these risk weightsare then used to determine the required level of regulatory capital.
Under the F-IRB approach, banksestimate the probability of default for credit exposures
using internal models. The probability of default is then used to compute risk weights,
which are then used to determinethe level of regulatory capital required.
A large body of literature has been developedaround probability of default models, and
the more recent literature has focused heavily on the application of machine learning
algorithms. Several noteworthy literature reviews in this space have shown that there is
great potential for the use of said algorithms to accurately estimate probability of default
(Alaka et al.,2018;Barboza et al., 2017;Butaru et al.,2016;Lessmann et al., 2015). Much of
these studies have been focused on effective creditscreening. However, in my work, I focus
on the improvement of capital allocation, which can improve banking efficiencies (Bitar
et al.,2018), improve banking prudence while reducing agency frictions (Anginer and
Demirguc-Kunt, 2014), reduce moral hazard conditions (Demirguc-Kunt et al., 2013) and
improve loan quality(Barth et al.,2004;Kopecky and VanHoose, 2006).
I argue and show that the standardizedapproach provides ineffective risk discrimination
across credit exposures, which provides sub-optimal regulatory capital allocation. I show
this by using four retail credit data sets (a credit card data set, a secured loan data set, a
mortgage data set (also secured loan) and an unsecured loan data set). For these data sets,
the IRB approach lends itself to more effectiverisk discrimination, which allows regulatory
capital to be more effectively allocated across exposures. Specifically, the IRB approach
JFRC
29,3
318
To continue reading
Request your trial