Offsite Bank Supervision Analysis of Bank Profitability, Risk and Capital Adequacy: A Portfolio Simu

1
Offsite Bank Supervision Analysis of Bank
Profitability, Risk and Capital Adequacy A
Portfolio Simulation Approach Applied to
Brazilian Banks Theodore M. Barnhill,
Jr.barnhill_at_gwu.eduMarcos Souto,
International Monetary FundBenjamin
Tabak,Banco Central do Brasil
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.

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.
  

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.

5
Synopsis

• Simulated means and standard deviations of
  returns on bank equity and assets are unbiased
  predictors of historical means and standard
  deviations.

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.

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.

8
Synopsis

• We demonstrated the significant potential risk
  measurement value of the Risk Bureaus data on
  bank credit risk distributions and sector and
  region loan concentrations.

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.
  

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.

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.)

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

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

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

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)

16
Table 1EWMA Volatilities
17
Table 2EWMA Correlations
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

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

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.

21
Table 9Brazilian Banks Balance Sheets
22
Business Loan Distributions
23
Consumer Loan Distributions
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 firms
• Debt to Value ratio
• Volatility of firm value

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 firms simulated market value of
  equity
• Calculate the firms simulated debt ratio (i.e.
  total liabilities/total liabilities market
  value of equity)
• Map simulated debt to value ratios into simulated
  credit ratings

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.

27
Table 3Distribution of Debt Ratios, Betas and
Firm-Specific Risk for Brazilian Companies by
Credit Risk Rating
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, 2003. Assessing Fiscal
  Sustainability under Uncertainty Journal of
  Risk, 2004)

29
(No Transcript)
30
(No Transcript)
31
Table 12 Simulated Capital Ratios
32
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 0,1
20 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 -0,0
34 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 0,0
91 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 0,0
49 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
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.

34
Conclusions

• 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

35
Conclusions

• 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.

36
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

37
Q A