Offsite Bank Supervision Analysis of Bank Profitability, Risk and Capital Adequacy: A Portfolio Simulation Approach Applied to Brazilian Banks Theodore M. Barnhill, Jr. barnhill@gwu.edu Marcos Souto, International Monetary Fund Benjamin Tabak, Banco Central do Brasil EFM 4_24_09

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. EFM 4_24_09

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. EFM 4_24_09

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. EFM 4_24_09

Synopsis Simulated means and standard deviations of returns on bank equity and assets are unbiased predictors of historical means and standard deviations. EFM 4_24_09

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. EFM 4_24_09

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. EFM 4_24_09

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. EFM 4_24_09

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. EFM 4_24_09

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. EFM 4_24_09

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.) EFM 4_24_09

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 EFM 4_24_09

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 EFM 4_24_09

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 EFM 4_24_09

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) EFM 4_24_09

Table 1 EWMA Volatilities EFM 4_24_09

Table 2 EWMA Correlations EFM 4_24_09

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 EFM 4_24_09

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 EFM 4_24_09

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. EFM 4_24_09

Table 9 Brazilian Banks Balance Sheets EFM 4_24_09

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

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

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 EFM 4_24_09

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 EFM 4_24_09

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. EFM 4_24_09

Table 3 Distribution of Debt Ratios, Betas and Firm-Specific Risk for Brazilian Companies by Credit Risk Rating EFM 4_24_09

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, 2003. “Assessing Fiscal Sustainability under Uncertainty” Journal of Risk, 2004) EFM 4_24_09

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Table 11 Simulated versus h istorical after - tax return on equity (ROE) and return on assets (ROA) EFM 4_24_09

Simulated Capital Ratios Table 12 Simulated Capital Ratios EFM 4_24_09

Simulated Capital Ratios Table 12 Simulated Capital Ratios Bank 4 4 5 5 6 6 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 EFM 4_24_09

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. EFM 4_24_09

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 EFM 4_24_09

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. EFM 4_24_09

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 EFM 4_24_09

Q & A EFM 4_24_09