Prudential management of Brazilian banks: Key variables under Basel III

Abreu, Daniel Pereira Alves de; Bressan, Aureliano Angel

doi:10.1590/1808-057x20262503.en

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Original Article • Rev. contab. finanç. 37 (101) • 2026 • https://doi.org/10.1590/1808-057x20262503.en linkcopy

Prudential management of Brazilian banks: Key variables under Basel III

Authorship SCIMAGO INSTITUTIONS RANKINGS

This study examines the determinants of capital adequacy in Brazilian commercial banks, focusing on how internal performance indicators from the CAMELS (capital adequacy, asset quality, management quality, earnings, liquidity, and sensitivity to market risk) framework and macroeconomic conditions shape prudential capital buffers under Basel III. Although capital regulation plays a central role in emerging economies, the literature offers limited evidence on the joint influence of CAMELS dimensions and macroeconomic factors on banks’ excess capital positions. Moreover, few studies explicitly address the dynamic and endogenous nature of prudential capital adjustment, particularly in the Brazilian context. Understanding the drivers of banks’ capital buffers provides insights into the effectiveness of Basel III in strengthening financial resilience and informs regulatory debates in developing economies. Quarterly panel data from 15 Brazilian commercial banks from 2018 to 2024 are analyzed using traditional panel estimators and dynamic approaches based on difference and system generalized method of moments, allowing for endogeneity, persistence, and intertemporal adjustment. The dependent variable is defined as the difference between the observed capital adequacy ratio and the regulatory minimum. The results indicate that asset quality and profitability positively affect capital buffers, while leverage and inflation exert robust negative effects. Other factors, including liquidity, market risk sensitivity, bank size, and economic growth, display specification-sensitive results, highlighting the role of dynamic adjustment and feedback effects. Overall, capital buffers reflect a prudential and dynamic process rather than purely contemporaneous decisions. The findings contribute to the prudential regulation literature and provide insights for regulators and bank managers regarding risk-based supervision and capital planning in emerging economies.

Keywords:
capital adequacy; CAMELS; capital requirements; Basel III

Aspect	Core capital	Tier 1 capital	Tier 2 capital
Quality	Highest quality	High quality, but includes hybrid instruments	Lower quality compared to tier 1
Components	Common shares, reserves, and retained earnings	Common equity tier 1 + hybrid instruments (additional tier 1), such as perpetual convertible bonds	Subordinated instruments, such as subordinated debt securities
Availability	Immediately available to cover losses	Available, but may be subject to conversion or write-down clauses	Available for loss absorption in the event of liquidation or bankruptcy
Loss absorption capacity	Absorbs losses continuously during operation	Absorbs losses in crises, depending on contractual clauses	Absorbs losses in the event of insolvency or liquidation
Examples of instruments	Common shares, profit, and capital reserves	Instruments comprising the principal capital and convertible perpetual securities	Subordinated debt securities with fixed maturity and limited amortization
Objective	Ensure operational stability and resilience	Provide additional support in times of crisis	Offer an extra cushion to absorb extreme losses

	Basel I (%)	Basel II (%)	Basel III (%)
Core capital	4.70*	2.00*	7.00-9.00
Tier 1 capital	5.50*	4.00*	8.50-11.00
Basel index	11.00	8.00	10.50-13.00

	Maximum limits (%)					Current buffer values (%)
	Until 2015	2016	2017	2018	From 2019	2024
Capital conservation	0.00	0.63	1.25	1.88	2.50	2.50
Countercyclical	0.00	0.63	1.25	1.88	2.50	0.00
Systemic	0.00	0.00	0.50	1.00	2.00	1.00*
Maximum total	0.00	1.30	3.00	4.80	7.00	3.50*

Segmentation	Type of financial institution	Characteristics
S1	Banking financial institutions	Size greater than or equal to 10% of GDP or engages in significant international activities
S2	Banking and non-banking financial institutions	Size between 10% and 1% of GDP
S3	Banking and non-banking financial institutions	Size between 1% and 0.1% of GDP
S4	Banking and non-banking financial institutions	Size less than 0.1% of GDP
S5	Non-bank financial institutions	Size less than 0.1% of GDP, and which do not carry out repurchase agreements, issue credit securitization securities, stock exchange transactions, or margin services, and which do not have assets subject to market risk

Dimension analyzed	Impact on CAR	Studies that concluded this impact
Liquidity	Positive	Aktas et al. (2015), Belém and Gartner (2016), Bhattarai (2020), Carvalho and Dantas (2021), Gharaibeh (2023), Hunjra et al. (2020), Shrieves and Dahl (1992)
Bank size	Negative	Bhattarai (2020), Thach and Pham (2022), Thuong et al. (2024)
Bank size	Positive	Aktas et al. (2015), Keqa (2021)
Inflation rate	Negative	Bhattarai (2020), Thach and Pham (2022), Thuong et al. (2024)
GDP growth	Positive	Mili et al. (2014), Schaeck and Čihák (2012)
GDP growth	Negative	Thach and Pham (2022), Thuong et al. (2024)
Management quality	Positive	Kwan and Eisenbeis (1997), Lotto (2018)
Earnings	Positive	Abba et al. (2018), Aktas et al. (2015), Bhattarai (2020), Gharaibeh (2023), Thach and Pham (2022)
Asset quality	Positive	Abba et al. (2018), Thach and Pham (2022)
Risk	Negative	Aktas et al. (2015), Carvalho and Dantas (2021), Hunjra et al. (2020), Keqa (2021), Lappay et al. (2021)
Risk	Positive	Abba et al. (2018), Belém and Gartner (2016)
Leverage	Positive	Gharaibeh (2023)
Leverage	Negative	Aktas et al. (2015), Thach and Pham (2022)

	Dimension	Variable	Formula
Internal variables	Asset quality	NPL	$\frac{N o n - P e r f o r m i n f L o a n s}{T o t a l L o a n s}$
	Management	OCR	$\frac{C o s t s a n d O p e r a t i o n a l E x p e n s e s}{O p e r a t i o n a l R e v e n u e s}$
	Earnings	RORA	$\frac{N e t P r o f i t}{R i s k W e i g t h e d A s s e t s}$
	Liquidity	LIQ	$\frac{H i g h L i q u i d i t y A s s e t s}{T o t a l A s s e t s}$
	Liquidity	LEV	$\frac{D e p o s i t s}{E q u i t y}$
	Sensitivity	RAR	$\frac{M a r k e t R i s k W e i g t h e d A s s e t s}{T o t a l A s s e t s}$
	Bank size	SIZE	$L N (A s s e t s)$
External variables	Economic growth	GDP	$∆ % G D P$
	Inflation	INF	$∆ % I P C A$
	COVID	COVID	Dummy = 1 from the second quarter of 2020 to the first quarter of 2021; otherwise, dummy = 0

Variable	Mean	Median	Minimum	Maximum	Standard deviation
DPCAR	0.3213	0.3175	-0.0814	0.7889	0.1261
NPL	0.0597	0.0524	0.0099	0.3260	0.0379
OCR	1.1658	1.0581	0.5660	8.6819	0.5790
RORA	0.0080	0.0071	-0.0122	0.0451	0.0058
LIQ	0.1513	0.1264	0.0080	0.5191	0.1015
LEV	11.2211	10.8314	2.4756	32.8324	5.0873
RAR	0.0226	0.0116	0.0001	0.2549	0.0386
SIZE	17.7990	17.2411	13.2842	21.7145	2.6258
GDP	0.0046	0.0045	-0.0264	0.0296	0.0087
INF	0.0131	0.0124	-0.0133	0.0317	0.0105

Panel A: correlation matrix
	DPCAR	NPL	OCR	RORA	LIQ	LEV	RAR	SIZE	GDP	INF	COVID
DPCAR	1
NLR	0.09*	1
OCR	-0.30***	0.05	1
RORA	0.05	-0.13***	-0.24***	1
LIQ	0.21***	-0.18***	-0.18***	0.01	1
LEV	-0.32***	0.22***	0.27***	-0.07	0.09*	1
RAR	0.11**	0.25***	-0.10**	-0.09*	-0.13***	-0.31***	1
SIZE	-0.12**	-0.13**	0.16***	0.06	0.00	0.43***	-0.34***	1
GDP	-0.11**	0.18***	0.01	-0.02	-0.27***	0.06	-0.04	0.10**	1
INF	-0.04	0.10**	0.10**	0.04	-0.09*	-0.03	0.03	0.01	-0.11**	1
COVID	-0.02	-0.01	-0.01	-0.07	0.14**	0.01	0.02	-0.01	-0.49***	0.07	1
Panel B: VIF
VIF		1.38	1.23	1.39	1.32	1.21	1.52	1.60	1.06	1.36	1.44

	Pooled	Fixed effect	Random effect
NPL	-0.9847***	-0.1925	-0.1680
OCR	-0.0386***	-0.0146*	-0.0157**
RORA	-0.0303	2.9194***	2.9037***
LIQ	0.2911***	0.1747***	0.1708***
LEV	-0.0104***	-0.0067***	-0.0060***
RAR	-0.1421	-0.2280	-0.2152
SIZE	0.0061**	0.0153	-0.0011
GDP	-0.1177***	-0.1150***	-0.0486*
INF	-0.6414	-0.6455*	-0.4506
COVID	-0.0431**	-0.0323***	-0.0124
Intercept	0.4458***		0.4079***
R²	0.2640	0.1860	0.1610
Adjusted R²	0.2450	0.1340	0.1440
Model F statistic	14.068***	8.658***	76.0540***
F-test for fixed effects		51.784***
Lagrange multiplier test		42.426***
Hausman test		23.620***
		Difference GMM	System GMM
NPL		-0.4767***	-0.7608**
OCR		-0.0193**	0.0389
RORA		0.9465*	6.3796***
LIQ		0.0809	0.6173**
LEV		-0.0163***	-0.0118
RAR		-0.9184***	0.4704
SIZE		-0.1066*	0.0155**
GDP		0.0190	0.0554
INF		-0.3384	-0.1493
COVID		-0.0013	-0.0023
Instruments (n)		15	15
Wald test		359.5476***	522.6983***
AR(1)		-2.1999**	-1.8016*
AR(2)		-1.2409	-1.4711
Hansen difference test		-1.4211

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Universidade de São Paulo, Faculdade de Economia, Administração, Contabilidade e Atuária, Departamento de Contabilidade e Atuária - Cidade Universitária Avenida: Professor Luciano Gualberto, 908 - FEA 3 - sala 118, CEP: 05508-010, Telefone: (+55 11) 2648-6320 - São Paulo - SP - Brazil
E-mail: recont@usp.br

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[TFN1] Source: Elaborated by the authors based on BCBS (2011).