1. Offsite Bank Supervision Analysis of Bank Profitability, Risk and Capital Adequacy: A Portfolio Simulation Approach Applied to Brazilian Banks Theodore M. Barnhill, Jr. Marcos Souto, International Monetary Fund Benjamin Tabak, Banco Central do Brasil
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2. Synopsis
Offsite bank supervision involves the continual monitoring of bank profitability, risk, and capital adequacy.
We demonstrate the value of integrated market and credit risk modeling techniques coupled with the focused collection and analysis of data on:
Correlated financial and economic environment market and credit risk drivers,
Bank asset and liability structures,
Sector and region loan concentrations / credit risk,
Interest rate and currency mismatches,
Borrower asset volatility / credit risk modeling.
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3. Synopsis
In the current study we implement an integrated market and credit risk portfolio simulation methodology on six unidentified Brazilian Banks.
These simulations utilize a significant dataset provided by the Risk Bureau of Banco Central do Brasil as well as publicly available information from other sources such as BankScope.
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4. Synopsis The study finds that:
Simulated credit transition matrices and loan default rates are very close to the historical ones estimated by the Risk Bureau.
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5. Synopsis
Simulated means and standard deviations of returns on bank equity and assets are unbiased predictors of historical means and standard deviations.
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6. Synopsis
A reduction in net interest margins for banks directly reduces bank profitability and increases risk, absent offsetting reductions in operating expenses or loan lose rates.
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7. Synopsis
Absent a decline in net interest margin or a default by the Government of Brazil most of the banks have a low failure probability.
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8. Synopsis
We demonstrated the significant potential risk measurement value of the Risk Bureau’s data on bank credit risk distributions and sector and region loan concentrations.
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9. Synopsis
We also demonstrate the significant potential of forward looking risk assessment methodologies as an offsite bank supervision tool to identify, and manage, potential risks before they materialize.
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10. Overview
Many institutions hold portfolios of debt, and derivative securities as well as direct equity and real estate investments which face a variety of risks including:
Credit,
Interest rate
Interest rate spread,
Foreign exchange rate,
Equity price,
Real estate price,
Commodity price, etc.
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11. Overview
Many of these risk factors are correlated with one another and may become more highly correlated during periods of financial stress
Ideally asset and liability portfolio risk assessments should account for all of these correlated risks (market, credit, Sovereign, inter-bank, etc.)
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12. Overview Various risk assessment methods are utilized:
Scenario Analysis
Simulation Modeling (which can be viewed as a very large number of scenarios with attached probabilities)
Value-at-Risk Analysis
Analytical Methods
Full Simulation Methodologies
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13. Overview
Current methodologies for assessing bank risk typically focus on either:
Market Risk (i.e. interest rate risk, exchange rate, equity price risk, etc.), or
Credit Risk (i.e. default risk, or credit rating migration risk)
Separation of market and credit risk in portfolio analysis results in misestimating overall A/L portfolio risk
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14. Methodology
By modeling a set of correlated systematic financial and economic risk drivers (interest rates, exchange rates, sector returns, etc.) the Monte Carlo portfolio simulation methodology we use allows for the integration of market and credit risk into one overall bank asset/liability portfolio risk assessment
If needed correlated equity, real estate, commodity price, Sovereign, and inter-bank risk can also be modeled
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15. Modeling the Financial Environment
Simulating Interest Rates (Hull and White, 1994)
Simulating Credit Spreads (Stochastic Lognormal Spread)
Simulating Equity Indices, Real Estate Price Indices, and FX Rates (Geometric Brownian Motion)
Simulating Multiple Correlated Stochastic Variables (Hull, 1997)
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16. Table 1 EWMA Volatilities
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17. Table 2 EWMA Correlations
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18. Methodology The portfolio risk assessment is accomplished by:
Simulating the future financial and economic environment at a pre-set horizon (e.g. 1 year) as a set of correlated stochastic variables (spot interest rates, exchange rates, GDP, sector equity indices, regional real estate price indices, etc.)
Simulating the correlated evolution of the credit rating and potential default for each security in the portfolio as a function of the simulated financial and economic environment stochastic variables
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19. Methodology
Revaluing each instrument included in the portfolio as a function of the simulated stochastic variables
Revaluing the total portfolio under the simulated conditions
Repeating the simulation a large number of times
Analyzing the distribution of simulated portfolio values to determine the risk levels
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20. Methodology
In the simulation, bank risk levels are driven by:
The volatility and correlations of the identified financial and economic risk drivers,
the distribution of credit qualities in the bank's loan portfolio,
the diversification of the loan portfolio across sectors and regions of the economy,
asset and liability maturity and currency mismatches,
the amount and diversification of equity and other direct investments across sectors of the economy and regions of the country.
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21. Table 9 Brazilian Banks Balance Sheets
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22. Business Loan Distributions
Panel A:
Distribution of Business Loans by Credit Quality. AA A B C D E F
G + H
Bank 1
0.02%
51.08%
21.25%
22.93%
4.05%
0.07%
0.42%
0.19%
Bank 2
34.28%
31.17%
19.92%
8.74%
1.90%
1.38%
0.76%
1.84%
Bank 3
41.58%
23.51%
7.97%
19.91%
2.41%
0.48%
2.75%
Bank 4
37.51%
27.70%
15.00%
4.99%
6.16%
3.50%
1.50%
3.65%
Bank 5
21.17%
36.70%
25.50%
7.74%
3.57%
2.02%
0.36%
2.94%
Panel B:
Distribution of Business Loans by Industry Sector.
Bank4
Ibovespa
0.70%
7.36%
8.90%
5.94%
4.71%
Aerospace
0.00%
0.09%
0.03%
0.75%
Basic Ind.
34.24%
29.36%
32.28%
42.64%
32.25%
Chemicals
0.04%
6.00%
4.10%
5.53%
5.11%
Cyc. Serv.
29.54%
23.22%
24.35%
26.12%
33.47%
Food Prd.
0.43%
9.55%
10.73%
10.33%
9.39%
Food Ret.
16.22%
1.49%
3.08%
3.06%
2.13%
Forestry
1.54%
2.55%
1.09%
2.82%
Paper
0.26%
0.56%
0.69%
0.86%
Mining
0.50%
0.14%
1.25%
0.28%
1.20%
Oil & Gas
12.32%
0.30%
0.05%
0.01%
Financials
0.88%
2.98%
1.58%
0.13%
Utilities
5.12%
17.70%
10.53%
4.18%
6.89%
Banco Central do Brasil Seminar

23. Consumer Loan Distributions
Panel A:
Distribution of Consumers' Loans by Credit Quality. AA A B C D E F
G + H
Bank 1
0.00%
89.16%
2.83%
2.88%
1.04%
0.76%
0.63%
2.71%
Bank 2
23.73%
42.90%
5.56%
10.84%
7.52%
1.58%
7.86%
Bank 3
0.43%
57.05%
4.58%
18.52%
3.78%
2.01%
1.64%
11.99%
Bank 4
76.77%
5.58%
4.11%
1.97%
1.72%
6.20%
Bank 5
0.01%
74.53%
5.69%
4.12%
2.16%
1.83%
1.63%
10.04%
Panel B:
Distribution of Consumers' Loans by Geographical Region.
Bank4
North
5.00%
5.09%
Northeast
22.50%
11.21%
Central
10.00%
12.93%
Southeast
40.00%
56.05%
South
14.72%
Banco Central do Brasil Seminar

24. Credit Risk Simulation Methodology
The conceptual basis is the Contingent Claims Analytical framework (Black, Scholes, Merton) where credit risk is a function of a firm’s:
Debt to Value ratio
Volatility of firm value
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25. Credit Risk Simulation Methodology
The following methodology is utilized to simulate loan credit rating transitions:
Simulate the return on an equity market index
Using either a one factor or multi-factor model simulate the return on equity for each firm included in the portfolio
Calculate the firm’s simulated market value of equity
Calculate the firm’s simulated debt ratio (i.e. total liabilities/total liabilities + market value of equity)
Map simulated debt to value ratios into simulated credit ratings
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26. Simulating the Equity Return of a Firm
Once the market equity return is simulated, the return on equity for the individual firms are simulated using a one-factor model (multi-factor models could be used too):
Ki = RF + Betai (Rm - RF) + iz
Ki = The return on equity for the firmi,
RF = the risk-free interest rate,
Betai = the systematic risk of firmi,
Rm = the simulated return on the equity index,
i = The firm specific volatility in return on equity,
z = a Wiener process with z being related to t by the function z = t.
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27. Table 3 Distribution of Debt Ratios, Betas and Firm-Specific Risk for Brazilian Companies by Credit Risk Rating
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28. Methodology
The Portfolio Simulation Approach has previously been applied to risk assessments for U.S. bond portfolios and bank risk assessments in a variety of countries:
Brazil
Japan
Slovakia
South Africa
U.S
It has also been applied to assessing debt sustainability for the Government of Ecuador (Barnhill, and Kopits, “Assessing Fiscal Sustainability under Uncertainty” Journal of Risk, 2004)
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29. EFM 4_24_09

30. Table 11
Simulated versus h
istorical
after -
tax return on equity (ROE)
and return on assets (ROA)
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31. Simulated Capital Ratios
Table 12
Simulated Capital Ratios
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32. Simulated Capital Ratios
Table 12
Simulated Capital Ratios
Bank
Interest Rate High Low High Low High Low
Mean 0,104 0,085 0,075 0,060 0,120 0,105
Standard Dev. 0,010 0,012 0,014 0,017 0,023 0,026
Maximum 0,124 0,108 0,111 0,100 0,177 0,165
Minimum 0,042 0,012 0,010 -0,012 -0,007 -0,034
VaR
99% 0,071 0,044 0,029 0,008 0,055 0,031
98% 0,077 0,050 0,039 0,019 0,063 0,039
97% 0,083 0,055 0,043 0,023 0,069 0,046
96% 0,085 0,058 0,045 0,026 0,074 0,051
95% 0,087 0,060 0,048 0,028 0,077 0,055
94% 0,088 0,062 0,050 0,031 0,081 0,059
93% 0,089 0,064 0,052 0,033 0,084 0,062
92% 0,090 0,065 0,054 0,035 0,086 0,064
91% 0,091 0,067 0,056 0,036 0,088 0,066
90% 0,091 0,068 0,056 0,037 0,090 0,068
75% 0,099 0,080 0,068 0,049 0,106 0,091
50% 0,105 0,088 0,077 0,063 0,122 0,109
25% 0,110 0,094 0,085 0,073 0,136 0,123
1% 0,119 0,103 0,099 0,088 0,162 0,150
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33. Conclusions
With detailed credit quality and sector and region concentration data, we believe that the portfolio simulation model has performed well in modeling Brazilian Bank risk and returns. With effort it can do better.
As with all models the results are highly dependent on the availability of appropriate and good quality data inputs. In this regard the data being collected by the Risk Bureau on bank loan portfolio credit quality and sector and region concentrations are of substantial potential value for undertaking forward looking risk assessments.
It would also be very helpful to have better data on the interest rate spreads on various credit quality loans.
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34. Conclusions Identification of banks with significant risk of failure
We believe this type of forward looking risk analysis has many useful applications for bank management and offsite bank supervision:
Identification of banks with significant risk of failure
Evaluation of financial institution capital adequacy
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35. Conclusions Governmental
We also believe this type of forward looking risk analysis can be used to identify potential preemptive actions to moderate risk levels:
Governmental
adopt monetary, economic, and regulatory polices that moderate financial and economic volatility.
Banks and/or Bank Regulators
change lending standards and portfolio credit quality
change the level of direct equity and real estate investment
change the sector and region concentration levels of the loan portfolio;
change asset/liability maturity and FX structure;
change capital levels.
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36. Finally the model has the potential to be extended to undertake:
Conclusions
Finally the model has the potential to be extended to undertake:
Estimation of systemic banking system risk (e.g. the risk of multiple bank failures in the same time period)
Integrated assessments of both banking system systemic risk and sovereign risk
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37. Q & A
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