Global Evidence on Capital Buffers and Net Stable Funding Ratios - - PowerPoint PPT Presentation

Global Evidence on Capital Buffers and Net Stable Funding Ratios

Oscar Carvallo Valencia Alberto Ortiz Bolaños CEMLA CEMLA and EGADE XI Seminar on Risk, Financial Stability and Banking Sao Paulo August 10-12, 2016

The views expressed in this presentation are those of the authors, and not necessarily those of CEMLA. We appreciate the contributions to this work of CEMLA’s economists Jonathan Barboza and Ignacio Garron.

Global evidence on capital buffers and net stable funding ratios

• Motivation: banking crisis are costly. Basel III, through capital and

liquidity regulations, tries to enhance the supervision and risk management of the banking sector. However, these regulations were calibrated mostly against BIS membership information. There is need to understand these variables at a global scale and undercover potential heterogeneities across countries and income groups.

• Objective: (i) understand the determinants of banks’ choice of

capital buffers, and (ii) analyze the role of banks’ net stable funding ratio (NSFR) in explaining future financial instability.

• Methodology: aided by theoretical frameworks, we designed

econometric specifications to test different hypotheses exploiting the information of almost 11,000 banks across 143 countries for the 2001 – 2015 period.

• Contribution: give a global analysis of the regulated variables by

examining a large sample of banks and countries while we factor in multiple dimensions, incorporate methodological improvements and conduct robustness checks.

Where to find the extended versions:

• Bank capital buffers around the world: cyclical patters and the

effect of market power. Oscar A. Carvallo and Alberto Ortiz.

• Net Stable Funding Ratio and Financial Stability: Global
• Evidence. Oscar A. Carvallo, Alberto Ortiz, Jonathan Barboza and

Ignacio Garrón.

• Both

available upon request with Oscar Carvallo (ocarvallo@cemla.org) or me (ortiz@cemla.org).

Costs of systemic banking crises in Latin America and the Caribbean

20 40 60 80 100 120

Argentina 1980 - 1982 Argentina 2001 - 2003 Brazil 1990 - 1994 Bolivia 1986 Chile 1976 Chile 1981 - 1985 Colombia 1982 Colombia 1998 - 2000 Ecuador 1982 - 1986 Ecuador 1998 - 2000 Haiti 1994 - 1998 Jamaica 1996 - 1998 Mexico 1981 - 1985 Mexico 1994 - 1996 Nicaragua 1990 - 1993 Panama 1988 - 1989 Paraguay 1995 Peru 1983 Uruguay 1981 - 1985 Uruguay 2002 - 2005 United Kingdom 2007 - United States 2007 -

Output loss (% of GDP) during systemic banking crises *

Source: Laeven and Valencia (2013). Systemic banking crisis database. IMF Economic Review 61: 225-270

* Cumulative sum of the differences between actual and trend real GDP over the period (T, T+3), expressed as a percentage of trend real GDP, with T the starting year of the crisis.

Banking crisis outcomes, 1970 - 2011

Country Output loss Increase in debt

Monetary expansion

Fiscal costs Fiscal costs Duration Peak liquidity Liquidity support Peak NPLs Medians % of GDP % of financial system assets In years % of deposits and foreign liabilities % of total loans All 23.0 12.1 1.7 6.8 12.7 2.0 20.1 9.6 25.0

Advanced

32.9 21.4 8.3 3.8 2.1 3.0 11.5 5.7 4.0 Emerging 26.0 9.1 1.3 10.0 21.4 2.0 22.3 11.1 30.0

Source: Laeven and Valencia (2013). Systemic banking crisis database. IMF Economic Review 61: 225-270

Banking regulatory framework

• "Basel III" is a comprehensive set of reform measures, developed by the

Basel Committee on Banking Supervision, to strengthen the regulation, supervision and risk management of the banking sector. These measures aim to:

– improve the banking sector's ability to absorb shocks arising from financial and economic stress, whatever the source – improve risk management and governance – strengthen banks' transparency and disclosures.

• The reforms target:

– bank-level, or microprudential, regulation, which will help raise the resilience of individual banking institutions to periods of stress. – macroprudential, system wide risks that can build up across the banking sector as well as the procyclical amplification of these risks over time.

• These two approaches to supervision are complementary as greater

resilience at the individual bank level reduces the risk of system wide shocks.

• It builds on the International Convergence of Capital Measurement and

Capital Standards document (Basel II).

Basel III phase-in arrangements

Bank capital buffers

• Bank capital buffers, which are holdings of banks’ capital-to-asset ratio in excess
• f the regulatory minimum, are persistent both across countries and over time.
• 20
• 10

10 20 30 40 50 60 70 80 2 4 6 8 10 12 14 16 18 20 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q75/Q25 range Average Capital Buffer Capital Buffer (right axis)

Developed

4,934 banks 37 countries average:8.6%

• What are the determinants of the observed levels of banks’ capital buffers?
• 20
• 10

10 20 30 40 50 60 70 80 2 4 6 8 10 12 14 16 18 20 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q75/Q25 range Average Capital Buffer Capital Buffer (right axis)

Following Jokipii and Milne (2008) we define the capital buffer as the bank capital ratio less the Minimum Capital Ratio (MCR). The bank capital ratio is approximated by the Total Capital Ratio (TCR), which measures the actual regulatory capital ratio in each jurisdiction. TCR data comes from Bankscope, MCR data from WB database “The Regulation of Banks around the World”, surveys I, II, III and IV. Barth et al. (2001, 2004, 2006, 2012).

Developing

2,184 banks 106 countries average:10.4%

Partial adjustment model

• The presence of highly persistent capital buffers could indicate that banks

approach their optimum target with a partial adjustment.

𝐶𝐶𝐶𝑗,𝑢 = 𝛽 𝐶𝐶𝐶

𝑗,𝑢 ∗ not observable

+ 1 − 𝛽 𝐶𝐶𝐶

𝑗,𝑢−1 +

𝜃𝑗 ⏟

bank−specific determinants

• The empirical model will be specified as:

𝐶𝐶𝐶

𝑗,𝑢 = 𝛽0 + 𝛽1𝐶𝐶𝐶 𝑗,𝑢−1 + 𝜀𝑗

𝑌𝑗,𝑢

• vector of variables

that determine 𝐶𝐶𝐶

𝑗,𝑢 ∗

+ 𝜃𝑗 + 𝑣𝑗,𝑢

• i.i.d error term

50 100 Capital Buffer 50 100 Capital Buffer(t-1) Developed Developing Linear regression Fitted values

𝐶𝐶𝐶

𝑢 ∗ = 𝔽𝑢 𝛾𝛭𝑢+1𝑆𝑢+1 𝑀

− 𝔽𝑢 𝛾𝛭𝑢+1𝑆𝑢

𝐸

𝛭𝑢 − 𝔽𝑢 𝛾𝛭𝑢+1 − 𝔽𝑢 𝛾𝛭𝑢+1𝑆𝑢

𝐸 − 𝐹

𝑀 𝑢

𝑠𝑠𝑠

𝐶𝐶𝐶

𝑗,𝑢 = 𝛽0 + 𝛽1𝐶𝐶𝐶𝑗,𝑢−1 + 𝜀𝑗

𝑌𝑗,𝑢

• vector of variables that

determine 𝐶𝐶𝐶

𝑗,𝑢 ∗

+ 𝜃𝑗 ⏟

bank−specific determinants

+ 𝑣𝑗,𝑢

• i.i.d error term

Source: Bankscope. Variable Description Hypotheses Total

Mean Sd. Obs. Buffer (BUF) Following Jokipii and Milne (2008) we define the capital buffer as the bank capital ratio less the Minimum Capital Ratio (MCR). The bank capital ratio is approximated by the Total Capital Ratio (TCR), which measures the actual regulatory capital ratio in each jurisdiction. 9.14 10.97 40,181

Bank variables

Bank size (Size) Logarithm of total bank’s asset Moral hazard motives could imply a negative relation with capital buffers, charter value a positive one. 14.20 2.08 40,053 Return Over Average Assets (ROAA) Net income/ average total assets According to the model, profitability is positively associated with the level

• f capital buffers.

0.75 2.20 40,103 Return Over Average Equity (ROAE) Net income / average total equity According to the model, profitability is positively associated with the level

• f capital buffers.

6.90 21.10 40,099 Boone Indicator (BOONE) Measure of competition linking marginal costs to market share Higher competition could lead to lower (EME) levels of buffers due to lower profitability and charter value, but if pool of borrowers is affected by competition, then could lead to higher (OECD) levels to cover losses.

• 0.05

0.12 35,649 Loan Loss Reserve Gross Loans (LLRGL) Non-performing loans / total bank assets The higher the ratio of non- performing loans as a percentage of total bank assets, the larger the capital buffers, a positive relation. 3.29 5.13 31,731 Cost of Funding (CF) Interest Expenses/ total funding According to the model, the cost of capital is positively associated with the level of capital buffers. 1.20 66.21 19,496

Sources: Bankscope for bank variables and World Bank database “The Regulation of Banks around the World”, surveys I, II, III and IV. Barth et al. (2001, 2004, 2006, 2012).

𝐶𝐶𝐶

𝑗,𝑢 = 𝛽0 + 𝛽1𝐶𝐶𝐶𝑗,𝑢−1 + 𝜀𝑗

𝑌𝑗,𝑢

• vector of variables that

determine 𝐶𝐶𝐶

𝑗,𝑢 ∗

+ 𝜃𝑗 ⏟

bank−specific determinants

+ 𝑣𝑗,𝑢

• i.i.d error term

Variable Description Hypotheses Total

Mean Sd. Obs.

Environmental variables

Growth rate of Gross Domestic Product (GGDP) Growth rate of Gross Domestic Product. According to the model, we expect to observe a negative (specially US and EME) relationship between capital buffers and GDP growth, a pro-cyclical behavior. 2.22 3.63 39,550 Money and quasi Money (MCM) Money and Quasi money (M2) as percentage of GDP Measures of financial depth associated with easiness of access to capital markets are expected to have a positive relationship with capital buffers. 119.33 69.69 35,662

Regulatory variables

Overall Capital Stringency (OCS) Index that measures the extent of regulatory requirements regarding the amount of capital banks must hold. Higher values indicate greater stringency. The stringency of capital regulation, the level of intervening power by authorities, and the enforcement of accounting standards, could be expected to have a positive relationship. 5.12 1.38 24,191 Official Supervisory Power (OSP) Index that measures the extent to which official supervisory authorities have the authority to take specific actions to prevent and correct problems. Higher values indicating greater power. 11.09 2.37 32,430 Bank Accounting (BACC) Dummy variable that takes a value of 1 when the income statement includes accrued or unpaid interest or principal on nonperforming loans and when banks are required to produce consolidated financial statements. 3.51 0.53 35,930 Moral Hazard Index (MHI) Moral hazard index based on countries deposit insurance scheme (Demirgüç- Kunt and Detragiache, 2002). Higher values indicate more moral hazard. The implicit insurance is expected to have a negative (EME) relationship with capital buffers. 119.33 69.69 35,662

Source: IFS for environmental and World Bank database “The Regulation of Banks around the World”, surveys I, II, III and IV. Barth et al. (2001, 2004, 2006, 2012).

We use the two-step system GMM estimators, with Windmeijer (2005) corrected standard error to account for potential endogeneity.

Summary of results:

determinants of banks’ choice of capital buffers

• The average bank in the world exhibits pro-cyclical behavior i.e.

capital buffers reduce during economic expansions. This would call for the extensive and effective implementation of Basel’s III proposed counter-cyclical buffer tools, at a global scale.

• Nevertheless, according to our empirical exercise, this average

result is conditional on a series of factors, some affecting the functioning of local markets, its regulation, the level of competition and several bank-specific characteristics.

• Costs of adjustment are important and do not seem to differ greatly

across levels of economic development.

• Large banks in global and domestic markets behave substantially

more pro-cyclically that their smaller peers.

• Cooperative and saving banks tend to hold higher capital buffers in

developed economies. Saving banks tend to behave counter- cyclically in the US.

Summary of results:

determinants of banks’ choice of capital buffers

• Cooperative and saving banks tend to hold higher capital buffers in

developed economies. Saving banks tend to behave counter- cyclically in the US.

• Banks account standards regulation, overall capital regulatory

stringency, bank risk and financial depth rise the capital buffers.

• More competition leads to higher buffers in developed countries but

to lower in developing ones.

• Higher deposit insurance moral hazard index is related to lower

capital buffers in developing countries.

• Pro-cyclicality seems to be a more generalized behavior in

developing, as in average, not only their larger banks but also the rest banks, tend to fluctuate negatively with the cycle.

Policy implications:

determinants of banks’ choice of capital buffers

Developed Developing

Policy implications:

determinants of banks’ choice of capital buffers

• Although in both regions capital buffers seem to become more counter-cyclical

after the global financial crisis, the road ahead regarding policy prescriptions have different starting points.

• For developed economies, we regard our empirical evidence as suggesting

playing more close attention to the pro-cyclical patterns of its largest banks.

– These banks are more systemically important given their relative position in the financial network and the potential cyclical externalities they may cause.

• With respect to developing economies, it would seem that the timing for

introduction of new macro-prudential tools is critical.

• It has been recognized the potential contractionary effect of capital regulation

(Repullo and Suarez 2013). This have to be taken in consideration.

• Pave the way for more transparent, competitive and deep financial markets,

stronger and effective supervisory power by authorities, all of which implies committed financial reform.

• This will probably guarantee appropriate conditions for the future implementation
• f more sophisticated macro-prudential tools, once the current factors behind

the growth volatility of such economies recede.

• Overall, heterogeneity should be taken into account, but be careful entering a

discretionary field subject to political pressures.

Liquidity regulation

• As a regulatory response to that crisis, in Basel III accord were

introduced two global liquidity standards for banks.

Liquidity Coverage Ratio (LCR) Net Stable Funding Ratio (NSFR)

Designed to promote short-term resilience of the liquidity risk profile of banks by ensuring they have sufficient high-quality liquid assets to survive a significant stress scenario lasting 30 calendar days. Designed to promote over-a-year resilience of the liquidity risk profile of banks by limiting overreliance

• n short-term wholesale funding to reduce the

mismatch between assets and liabilities.

𝑂𝑂𝐶𝑆𝑗,𝑢 = Available Stable Funding𝑗,𝑢

Required Stable Funding𝑗,𝑢

• 1
• .5
• 1

.25 .5 .75 1 1.25 1.5 1.75 2 50 100 150 200 Countries 50 100 150 200

Q75/Q25 NSFR range Median NSFR NSFR=1 NSFR Status change for at least one bank (right axis) Inverse of probability of status change (right axis)

NSFR (average 2001-2007) Status change (2008-2015)

Net stable funding ratio and financial stability

Source: Bankscope, World Bank and own calculations.

Our empirical approach

• NSFR
• Leverage
• Size
• Growth Loans Deviation
• Mortgage Loans Deviation
• ∆Loans (to GDP)
• ∆Mortgage Loans (to GDP)
• Change in exchange rate
• M2 (to GDP)
• Government owned Banks
• Overall Supervisory Power
• Overall Restrictions on

Banking Activities

• Boone Indicator
• Developing
• Z-score

(average 2008- 2013)

• Status change

between 2006- 2015

Financial Crisis

Vulnerabilities (variables in average 2001-2007) Financial Risk (span 2008-2015)

• International demand for safe US assets (global

imbalances

• Government subsidies on mortgages
• Deregulation, regulatory arbitrage
• Growing wage inequality
• Higher house prices

Triggers 2008-2015

Summary of results:

net stable funding ratio and financial stability

• Our main finding is that the net stable funding ratio (NSFR) is a robust

predictor of probability of failure and financial instability, competing with

• ther important predictors in the literature (leverage, credit growth, credit

gaps and overvaluation of the currency).

• It has additional predictive ability over credit measures as indicated by our

area under the ROC curve analysis.

• Exchange rate overvaluation, when combined with low stable funding

carries out larger probability of failure.

• Regulation restricting the scope of bank activities increases probability of

failure and financial instability.

• Financial instability and probability of failure is lower for financial banking

systems with a larger share of foreign and government banks, and higher financial depth.

• NSFRs are lower in developing economies, tough the incidence of default is

lower during the whole period. Also, the effect of NSFR on default is three times larger in developed economies .

• As occurs in other empirical literature, macroeconomic conditions were

significant.

Policy implications:

net stable funding ratio and financial stability

• Our empirical evidence provides strong support for Basel regulation
• n structural liquidity (NSFR).
• While non-stable funding is a critical factor for policy actions by

itself, its interaction with the external sector of the economy should be paid close attention.

• However, other potential sources of non-stable funding, both

domestic and external, from other formal markets and institutions or from the shadow banking, should be also the object of regulation.

• Other relevant issue are the implicit or explicit public guarantees,

specially for the long-run help of financial system.

• Recognize the importance of heterogeneity for policy design, but

don’t forget the rules versus discretion lessons.

Global Evidence on Capital Buffers and Net Stable Funding Ratios

Oscar Carvallo Valencia Alberto Ortiz Bolaños CEMLA CEMLA and EGADE Thank you

The views expressed in this presentation are those of the authors, and not necessarily those of CEMLA. We appreciate the contributions to this work of CEMLA’s economists Jonathan Barboza and Ignacio Garron.