financial stability & systemic risk in brazil · financial supervision: monitors systemic risk...

1

Luiz Awazu Pereira da Silva

September 2012

G-20 Conference on Financial Systemic Risk

Istanbul

Financial Stability &

Systemic Risk in Brazil:

Ensuring Stability in a “New

Normal” Environment

2

Outline: Financial Stability & Systemic Risk in Brazil

1. What is the IMF-FSAP 2012 exercise saying?

2. What is the BCB’s Regulation/Supervision doing?

3. Can we (should we?) “Target” Financial Stability

using a synthetic indicator of FSSR?

4. Financial Stability in a “New Normal” environment

with Sudden Stops and Floods

5. Conclusions

3

1.What is the IMF-FSAP

2012 exercise saying?

4

http://www.imf.org/external/country/bra/index.htm

http://www.imf.org/external/country/bra/index.htm

5

Gross Debt by Sector Compared to Eurozone

0

50

100

150

200

2502

00

7

20

10

20

07

20

10

20

07

20

10

20

07

20

10

20

07

20

10

20

07

20

10

20

07

20

10

20

07

20

10

Governments

Non-Financial Firms

Households

Eurozon Germany France Italy Spain Portugal Greece

Brazil

Eurozone:

161%

Brazil:

98%

6

IMF financial system assessment (Brazil 2012 FSAP)

• Brazil’s financial system has grown a lot since last exercise (2002)

• In size, strength, diversification and sophistication

• Financial system solid in main dimensions

• Capital requirements, profitability, loan loss provisions and liquidity

• Financial system resilient

• Stress tests: resiliency to extreme shocks, including to severe global

recession

• Limited exposure to external risks

• Low bank foreign exposure in assets and funding, conservative prudential

regulation, sudsidiarization approach to foreign capital banks

• Strong, sophisticated and intrusive bank supervision

• Adequate tools for prevention and intervention

• Brazilian supervision was one of the best evaluated among G20 countries

• SPB adopts best international practices

• Allows constant monitoring of SFN and its risks

7

2. What is the BCB’s

Regulation/Supervision

doing?

8

Central Bank of Brazil – Main Activities National Financial System

Monetary

Policy

Financial

Regulation

Financial

Supervision

Wide scope of Central Bank’s authority helps

policy coordination

9

The National Financial System

Source: BCB

Type 2007 2008 2009 2010 2011 2012*

Multiple bank 135 140 139 137 139 138

Commercial bank 20 18 18 19 20 22

Development bank 4 4 4 4 4 4

Savings bank 1 1 1 1 1 1

Investment bank 17 17 16 15 14 15

Exchange bank 2 2 2

Consumer finance company 52 55 59 61 59 58

Securities brokerage company 107 107 105 103 99 95

Exchange brokerage company 46 45 45 44 47 54

Securities distribution company 135 135 125 125 126 121

Leasing company 38 36 33 32 31 30

Real estate credit company and savings and loan

association 18 16 16 14 14 12

Mortgage company 6 6 6 7 8 7

Development agency 12 12 14 15 16 16

591 592 581 579 580 575

Credit cooperative 1,465 1,453 1,405 1,370 1,312 1,273

Micro-entrepreneur credit company 52 47 45 45 42 40

2,108 2,092 2,031 1,994 1,934 1,888

Consorcio company 329 317 308 300 284 245

Total 2,437 2,409 2,339 2,294 2,218 2,133

*Aug 12

Financial Institutions (over 2,000) by Type

10 Source: BCB

Top 10 Financial Institutions National Financial System

Name Ownership Total

assets

(BRL billion)

Credit and

leasing

operations (BRL billion)

Total

deposits

(BRL billion)

Net

worth

(BRL billion)

Employees

(Thousand)

Branches

(#)

Basel

capital

ratio (%)

Banco do Brasil Federal government owned 998 429 468 63 131 5,318 14.5

Itaú Domestic private 838 305 240 77 122 3,871 16.6

Bradesco Domestic private 723 249 217 64 100 4,659 16.8

BNDES Federal government owned 630 224 21 56 3 1 19.5

Caixa Federal government owned 596 298 285 21 111 2,412 12.9

Santander Foreign controlled private 448 181 122 66 54 2,535 21.9

HSBC Foreign controlled private 148 47 63 9 30 868 13.5

Votorantim Domestic private 116 55 23 9 2 37 15.5

Safra Domestic private 91 42 13 6 6 106 13.1

BTG Pactual Foreign participation private 82 5 17 9 1 7 18.7

Others 818 317 266 107 71 1,805

Banking segment total 5,490 2,151 1,736 489 632 21,619

Jun 12

11

• All FIs regulated and supervised

• Financial regulation mostly infra-legal

• Convergence to international standards (IFRS,

Basel 2, 2.5 and 3, IOSCO)

• Participation in international forums (BCBS, G20,

FSB)

• Rigorous financial regulation

General Features Prudential Regulation and Supervision

12

• Risk-based approach to supervision

• On-site supervision

- frequent on-site examinations

- rating of supervised institutions

– qualitative assessment of risk management and control

– analysis of financial and economic indicators

– identification of areas to be monitored

• Includes contingency planning and assessment

of organizational structures dedicated to risks

Financial Supervision - Main Features Prudential Regulation and Supervision

13

• Specific monitoring of market and liquidity risks

- uses information on assets and derivatives registered

in clearing houses

- conciliation with FIs’ accounting information

• Monitoring of aggregate evolution of systemic

risk over time

• Periodic application of stress tests to FIs’

statements

Off-Site Supervision Prudential Regulation and Supervision

14

• Central Bank has detailed information about all

credit operations above BRL 1,000 (~USD500):

- 289 million credit operations with detailed information

- 45 million debtors

- Covers 96% of Brazilian banking credit market

- Information shared with banks, enabling better risk

assessment

- Allows Central Bank to carry out in-depth and timely

analysis of the financial system’s and individual bank’s

loan portfolios

Comprehensive Overview of the Credit Market Prudential Regulation and Supervision

15

• Minimum capital ratio: 11% of RWA (above Basel

minimum of 8%)

• Capital requirement for credit risk on trading book

exposures

• Lower risk weights for residential property exposures

conditioned to loan-to-value

- LTV < 50% 35% risk weight

- 50% < LTV < 80% 50% risk weight

- 80% < LTV < 100% 75% (retail) or 100% (otherwise)

• Highest multiplier for standardized market risk

requirement

• Forward-looking provisioning rules

Rigorous Regulation Prudential Regulation and Supervision

16

• Mandatory organizational structure for

management of each risk factor

- Credit, market and operational risks

- Board accountability on risk management

• Limits on large exposures

• Limits on foreign currency exposures

• Mandatory registration of OTC derivatives

• Mandatory internal controls


17

• Provisions based on expected and incurred

losses, reducing procyclicality

• Full provisioning is required 6 months after

delinquency (fast provisioning)

• Write-offs are required 6 months after full

provisioning

• Central Bank applies the most conservative

credit risk classification possible for each

borrower (conservative provisioning)


18

Financial Supervision: monitors systemic risk

• Monitoring FSSR thru several indicators of the

Financial System: capital, credit, NPLs, provisions, etc.

- Monitoring of Liquidity Conditions

- Macroeconomic Stress Testing

- Contagion Risk Monitored thru Network Analysis for

Systemic Resilience

• Institutions & Reporting:

- Financial Stability Reports (FSRs)

- Financial Stability Committee (FSC)

19

Data Collection Systems at the BCB

Financial Institutions

Daily -Bank Reserve Deposits -Foreign exchange transactions -Statement of capital requirements

Monthly -Statement of Financial Risk Management: (assets / liabilities and off balance sheet) -Balance Sheets -Statement of Liquidity Risk -Statement of operational limits -Credit portfolio

Quarterly -Relevant investments

Whenever changes

-Ownership structure

Clearings and Depository trust Companies - Daily

-Interbank Deposits -Derivatives -Securities

20

Financial Stability Indicators Used by Supervision

1,8 1,7

25,5

14,1

15,5 16,6

Jun 2008

Dec Jun 2009

Dec Jun 2010

Dec Jun 2011

Dec Jul 2012

Liquidity Index

Return on Equity

Capital Ratio: Basel Index

Source: BCB

21

Financial Stability Indicators Used by Supervision

Source: BCB

19,3

28,0

34,8 16,2

3,6 3,9

1,53 1,62

Jun 2008

Dec Jun 2009

Dec Jun 2010

Dec Jun 2011

Dec Jul 2012

Average Maturity of Credit Portfolio

Growth of Credit Portfolio

Delinquency Rate

Coverage Index: Provisions/Non-performing Loans

22

Financial Stability Indicators: Brazil (red) & Others

0

4

8

12

16

20

Total Capital RatioLatest available data

%

Source: IMF (financial stability indicators)

23


0

3

6

9

12

Non performing loans to total loansLatest available data %


24


-15

0

15

30

45

60

75

Non performing loans net of provisions to capitalLatest available data

%


25



0

50

100

150

200

Liquid assets to short term liabilitiesLatest available data %

26

0

15

30

45

60

Liquid assets to total assetsLatest available data %


27

• Macroeconomic stress: the system (thru its provisions &

capital) would be able to absorb the deterioration of the

main macroeconomic indicators.

• Sensitivity analysis: for 3 major risks (credit, foreign

exchange and interest rate), the analysis shows that there

would be NO cases of insolvency, even in situations of

extreme volatility.

• Direct contagion: the failure of any individual bank does

not lead to widespread failure of other banks, indicating low

direct contagion.

Stress Tests: findings

28

Stress Test Results – Macro Stress March 2011

0

3

6

9

12

Mar2011

Jun Sep Dec Mar 2012

Jun Sep

VAR Model (Stressed)

Focus Report (Worst Cases - 95% CI)

Market Consensus (Focus)

Provisions (Mar/2011)

%Macroeconomic Stress Test - Estimated NPL

9

12

15

18

Mar2010

Jun Sep Dec Mar 2012

Jun Sep

Macroeconomic Scenario

Macroeconomic stress test – Basel Index

%

Ad Hoc scenario

29

4

5

6

7

8

Dec

2007

Dec

2008

Dec

2009

Dec

2010

Mar

2011

% of Total

Loans

Estimated Needs versus Actual Provisions

Worst Classification (Inter-bank) Current Provision (System)

Stress Test Results – Apply Worst Rating March 2011

30

Stress Test Results – March 2011

31

Stress Test Results – March 2011

32

Direct Contagion and Network Analysis

Credit risk exposures between economic-financial conglomerates’

counterparties.

Real Network

Not limited to interbank deposits and not an estimation Network

Test covers: Time deposits, loans, debentures, swaps, exchange rate

operations mismatches, options, credit cessions with recourse

negotiated between financial institutions, repos under which collateral

is delivered by a company of the conglomerate.

Simulation:

Take off one bank (or a set of banks) and see what happens to the

system (domino effect = direct contagion).

33

In the worst case, if one of the (5) large big bank crashes, it would cause 25 banks failures (out of over 2000 banks) by contagion. But those 25 banks represent 0.7% of system assets. Why is direction contagion so limited? – Large capital buffers above Minimum Regulatory C -Regulatory limits – direct exposure limited to 25% of Regulatory Capital. -Interbank market characteristics: about 93% of the market transactions are collaterized.

Direct Contagion and Network Analysis

34

Creditor Claim

on Exp/

Cap Amount (R$) Large Bank 1 SMS banks 5,2% 4.743.150.925

Large Bank 2 SMS banks 1,8% 1.895.187.423



Large Bank 5 SMS banks 0,2% 23.642.920


Exposures in Brazil’s of 5 Largest to 12 Small Banks

35 35

Interconnectedness index ()

3.430

3.435

3.440

3.445

3.450

3.455

3.460

Mar-07 Sep-07 Mar-08 Sep-08 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11

The 25 more connected institutions provide over 90% of total transfers supporting the hypothesis that Brazilian banking system interconnectedness has a fat tail distribution: few institutions are highly connected (exposures are collateralized) and, thus, are key in the interbank exposure network

Interconnectedness diagrams of the Brazilian banks (2011Q4) 25 institutions (R$ 2.802 trillion)

The degree of interconnectedness of the whole system can be summarized by an index () proportional to the size of the left tail of the distribution of interconnectedness across financial institutions. When () increases there are more highly connected institutions, indicating that the net is more concentrated (and thus the systemic risk has increased, since an institution highly connected impacts several other institutions in the case of a default).

Interconnectedness index ()

36 36

25,8 26,0 24,6 25,7

28,3 30,9

35,2

40,5 43,7

45,2

49,0 50,6

-5

5

15

25

35

45

55

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012*

% o

f G

DP

2009-2011: 18.3% (average growth of nominal credit)

2005-2008: 25.2% (average growth of nominal credit)

*Jun 12

Source: BCB

Credit / GDP : Steady & Sustainable Growth

12 months through

August 2012: 16.2%

37 37

-15

0

15

30

45

60

75

Dec2006

Jun2007

Dec Jun2008

Dec Jun2009

Dec Jun2010

Dec Jun2011

Dec

Credit portfolio annual growth

Private Control Foreign Control

Government Control (Commercial) Government Control (Development)

Credit Growth (% p.a.) by ownership of Bank

38

0%

15%

30%

45%

60%

Dez

2005

Dez

2006

Dez

2007

Dez

2008

Dez

2009

Dez

2010

Dez

2011

yoyCredit growth

Investment Consumer Housing

Credit Growth (% p.a.) by loan type

39 39

0,4

0,5

0,6

0,7

0,8

Jan2008

Jul Oct Jan2009

Apr Jul Oct Jan2010

Apr Jul Oct Jan2011

Apr Jul Oct

Average LTV

Real Estate - Loans to individualsBank: Caixa Econômica Federal

9 to 10 years 10 to 15 years 15 to 20 years 20 to 25 years 25 to 30 years

Housing financing average LTVs

40

Credit Market (Banks) External Vulnerabilities

Cross-border Banking Exposures

(% of GDP, as of September

2011)

41

Brazil’s Banks Exposure to Eurozone Periphery

0 20 40 60

Portugual

Belgium

France

Germany

Netherlands

Spain

United Kingdom

Ireland

Denmark

Austria

Mexico

Italy

United States

Brazil In percent of total lending

42

• Data Availability: extensive registration & reporting

system.

• Stress-testing & Sensitivity analysis: quality of tests

depends on data availability.

• Important Indicators: (1) Capital, provisions & liquidity to

absorb (large) shocks; (2) Credit Growth vis-à-vis (some

measure) of Equilibrium; (3) Risk of Contagion thru

Interconnectedness.

Conclusions for Monitoring FS & SR

43

Macroprudential instruments by vulnerability and financial system component (source: CGFS)

Balance sheet* Lending contract

capital ratio LTV cap

risk weights debt service / income cap

provisioning maturity cap

profit distribution restrictions margin/haircut limit

credit growth cap

liquidity / reserve requirements

FX lending restriction

currency mismatch limit

open FX position limit

concentration limits

systemic capital surcharge

subsidiarisation

exchange

trading

Interconnect

edness

central

counterparties

(CCP)

Vu

lne

rab

ilit

y

Leverage

Liquidity or

market riskvaluation rules (eg. MMMFs)

local currency or

FX reserve

requirements

central bank

balance sheet

operations

Financial system component

Individual Bank or deposit-taker Non-bank

investor

Securities

market

Financial

infrastructure

After Monitoring FS & SR : When, How Use MaPs?

44

3. Can we (should we?)

“Target” Financial

Stability using a

synthetic indicator?

45

Monetary Policy

(MP) One Instrument:

CB Base Rate

Macro-Prudential

(MaP) Various Instruments:

RR, LTVs, DTIs, K req

(Basel rules), etc

Price Stability

(Inflation)

Effects on

Activity/Inflation

well-known

Effects on

Activity/Inflation

less known

Financial

Stability

(Risk)

Effects on Risk

(credit & asset

excess growth)

less known

Effects on Risk

(credit & asset

excess growth)

well-known

Why? 2 Objectives (Price S & FS) & 2 Instruments

46

Monetary Policy

(MP) One Instrument:

CB Base Rate

Macro-Prudential

(MaP) Various Instruments:

RR, LTVs, DTIs, K req

(Basel rules), etc

Price Stability

(Inflation)

Rate Hikes to

Moderate

Activity/Inflation

Lean against

real economy

cycle

Financial

Stability

(Risk)

Lean against

financial

cycle

Tighter Prudential

Regulations to

Moderate Credit &

Asset Growth

Credibility using MP + MaP to moderate cycles?

47

http://www.bcb.gov.br/?WORKINGPAPERS

http://www.bcb.gov.br/?WORKINGPAPERS

48

Anchoring Expectations using one indicator?

Monitoring several indicators is important and provides early

warning signals of the many dimensions of F(I)S & SR.

What about using a single synthetic indicator? Can a FSSR

Framework (with its FS Committee) mimic the IT Framework

(and its MPC)? Would it improve coordination /

complementarity between Macroprudential Policies (MaP) and

Monetary Policy (MP) to ensure both FS and macro stability?

We group into one 3 CGFS indicators of vulnerability: (i)

leverage and credit (proxy Credit-to-GDP gap); (ii) liquidity and

funding (proxy Loan-to-deposit); (iii) resilience of the market

structure (proxy interconnectedness index ()). Many other

possibilities / combinations are open for testing

49

Individual components Synthetic indicators

-3

-2

-1

0

1

2

3

I II III IV I II III IV I II III IV I II III IV I II III IV

2007 2008 2009 2010 2011

Credit-to-GDP gap (normalized)LTD ratio - 1 (normalized)Interconnectedness index (normalized)

-3

-2

-1

0

1

2

3


2007 2008 2009 2010 2011

S1 S2 S3

Synthetic Indicator of Financial Stability

S1: simple average of our normalized proxies for leverage, liquidity and

interconnectedness, representing both time and cross-sectional dimensions of

risk.

S2:captures common risk factors of the individual systemic risk indicators using

Principal Component Analysis (PCA)

S3: weighted average of the first and second principal components

+Instability

+Stability

50

Link Indicator S2 of FS with Probability of Distress

Synthetic Indicator (S2)

of Financial Stability & Systemic Risk (FSSR)

Probability of Distress

Indicator 1

Leverage/Credit

Indicator 2

Liquidity

Indicator 3

Interconnectedness

Distribution of Synthetic Indicator (S2)

Around Equilibrium Value

Definition of Threshold

of Policy-Maker’s Tolerance

51

Equilibrium of Synthetic Indicator S2 for FS

Potential Growth Rate for Credit: obtained from general equilibrium

exercise/model or from data filtering (HP, etc.)

The “equilibrium” credit growth is the potential credit growth which is

compatible with a null synthetic indicator S2. In other words, the

“equilibrium” is defined as the credit growth rate which is compatible with

(1) a zero credit-to-GDP gap, (2) a loan-to-deposit ratio equal to one, and

(3) an interconnectedness index equal to its unconditional mean.

Probability Density Functions

Synthetic indicator (S2) Potential credit growth (% annual)

.00

.05

.10

.15

.20

.25

.30

-4 -3 -2 -1 0 1 2 3 4

De

nsity

0

2

4

6

8

10

12

14

16

.08 .10 .12 .14 .16 .18 .20 .22 .24 .26

De

nsity

Source: Author´s calculations. Densities estimated via Epanechnikov kernel. Sample 2007Q1-2011Q4.

52

The synthetic indicator S2 can provide an important piece of information for a policy-

maker that is the probability of a financial stability disruption at a given time period.

Using the synthetic indicator S2: conditional model using the individual risk factors

(leverage, liquidity and interconnectedness) as covariates, and estimate using quantile

regression (QR) to generate conditional density functions and, therefore, calculate the

probability of a disruption.

QR model: S2t = 0(t) + 1(t)*leveraget + 2(t)*liquidityt + 3(t)*interconnectednesst +

b(t)*xt for a grid of selected quantiles t[0;1].

Control variables (xt) considered in the model: real GDP growth rate, non-performing

loans to total gross loans, and international reserves to short-term external debt ratio.

Then, the estimated conditional quantiles are mapped into the zero-one interval (i.e.,

logit-transformed) and the respective probability density functions (PDF) are estimated

for selected quarters via Epanechnikov kernel

Estimating Probability for Synthetic Indicator

53

0.0

0.2

0.4

0.6

0.8

1.0

I II III IV I II III IV I II III IV I II III IV

2008 2009 2010 2011

S2 quantile 0.10S2 quantile 0.90

Probability of “Financial Stability Disruption”

Estimated conditional quantiles for the (logit-transformed) Synthetic Indicator S2

54

Probability Density Functions (PDF) of S2 at selected quarters

2009Q4 2010Q4

0

1

2

3

4

5

6

7

8

0.60 0.64 0.68 0.72 0.76 0.80 0.84 0.88 0.92 0.96 1.00

C1

C2

0.0

0.4

0.8

1.2

1.6

2.0

2.4

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

C1

C2

Source: Author´s calculations. Densities estimated via Epanechnikov kernel.

The probability of disruption is computed (for each period) by identifying the

respective quantile which corresponds to a selected threshold value.

For example, we define the probability of a financial stability “disruption” as

the probability of the (logit-transformed) synthetic indicator S2 surpassing (in

each period) the threshold level d = 0.80 (or, alternatively, d =0.85, provided

that S2[0;1])

Probability of “Financial Stability Disruption”

55

The early identification of

specific (projected) points

in time where the

probability of disruption

rises above a tolerance

threshold would be an

indication for the

Financial Stability

Committee of a threat to

financial stability,

suggesting the need for

policy action including

through macroprudential

(new) rules and the

strengthening of

regulations.

0

10

20

30

40

50

60

70

80

0

2

4

6

8

10

I II III IV I II III IV I II III IV I II III IV

2008 2009 2010 2011

Probability of FS Disruption (delta = 0.80)Probability of FS Disruption (delta = 0.85)Non-performing Loans to Total Gross Loans (%)

Probability of “Financial Stability Distress”

Note: Probabilities on left-hand scale and NPL ratio on right-hand scale.

Threat to stability

Stability

Tolerance threshold

Prob. Of Distress

56

4. Financial Stability

in a “New Normal”

environment with

Sudden Stops and

Floods

57

Capital Inflows, Real Credit Growth, and Real Equity Prices, 1996-2010

Source: International Monetary Fund, Global Financial Stability Report (April 2011)

-2,5

-2,0

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

-2,0

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

2,5

96 98 00 02 04 06 08 10

Asia

real credit

real equity prices

capital flows (right scale)

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

96 98 00 02 04 06 08 10

Latin America

capital flows

58

FSSR in Post-QEs: Dealing with Excessive K Flows

Unusually intense & volatile

capital flows to EMEs

Exacerbate financial pro-cyclicality (boom and

bust cycles) in local credit and asset

markets (including FX)

Policy response(s)?

Financial (Ins) Stability Economic (Price) Stability

59 59 59

New Normal: Increase in global liquidity; affects volume

and intensity of capital flows into EMEs; increases

volatility (risk-on & risk-off / sudden stops & floods);

affects macro financial conditions in EMEs (inflation,

asset prices, credit)

Complicates Aggregate Demand Management (MP

together with FP) probably needs to be complemented by

MaPs

MaPs of type 1 (Mitigate Risk and Act as Counter-

Cyclical Policy; e.g., CC K buffers in Basel III)

MaPs of type 2 (Affects openness of K account)

Policy Responses to Manage FSSR in New Normal

60

5. Conclusions

61

FSSR requires data, framework, monitoring procedures

tools (MaP) and institutional arrangements for

Supervision, etc.)

Turning MaP operational is a major policy challenge,

especially now in the “New Normal”

When and how should MaP be activated? As required by

each indicator of SR? In a coordinated way with other

policies? Complementing other (e.g., monetary) policies?

If MaP operate in an IT framework context, how should

changes be announced? Communicating with a synthetic

index of FSSR conditions might help? If so which one?

62

Thank you

63

Annex

64

Correlations with Credit-to-GDP gap Synthetic indicators

-3

-2

-1

0

1

2

3


2007 2008 2009 2010 2011

S1 S2 S3

Synthetic Indicator of Financial Stability

S2 S3

Output gap -0.36 -0.17

Industrial production gap -0.39 -0.21

Credit-to-GDP gap 0.82 0.79

Credit growth gap (financial system) 0.45 0.39

Credit growth gap (earmarked) -0.37 -0.34

Credit growth gap (non-earmarked) 0.54 0.49

Credit growth gap (housing) -0.60 -0.59

Credit growth gap (individuals-consumption) -0.48 -0.36

Credit growth gap (corporations-total) 0.51 0.43

Credit growth gap (corporations-external funding) 0.51 0.41

Credit growth gap (corporations-domestic funding) 0.38 0.32

financial stability & systemic risk in brazil · financial supervision: monitors systemic risk...

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