Manajemen Risiko Lembaga Keuangan

1. Budi Purwanto Manajemen Risiko Lembaga Keuangan

2. Ikhtisar Bagian ini membahas risiko-risiko dalam intermediasi keuangan, antara lain: Risiko suku bunga; Risiko nilai tukar; Risiko pasar; Risiko operasional; Risiko politik (country risk); Risiko kredit; Risiko likuiditas; dan Risiko permodalan (insolvency risk).

3. Risiko Suku Bunga Risiko suku bunga akibat intermediasi: Ketidakcocokan jatuh tempo aktiva dan pasiva. Ketidakcocokan merupakan masalah sistematis dalam intermediasi keuangan. Risiko pendanaan-kembali. Risiko investasi-kembali.

4. Risiko Nilai Tulkar Tingkat imbalan dalam mata uang domestik dan valas tidak selalu berkorelasi sempurna. Nilai tukar valas mungkin tidak berhubungan. Misal: US$/IDR mungkin menguat sementara �/IDR melemah. Konsentrasi pada valas tertentu dapat menimbulkan risiko nilai tukar.

5. Risiko Pasar Risiko pasar terbuka dalam pertukaran aktiva dan pasiva (dan turunannya). Contoh: sub-prime mortgage. Cenderung lebih besar ketahanannya pada pertukaran pendapatan daripada perdagangan tradisional dalam meningkatkan paparan pasar.

6. Risiko ekonomi makro Kenaikan inflasi atau volatilitasnya. Keduanya mempengaruhi suku bunga. Peningkatan pengangguran Mempengaruhi risiko kredit.

7. Risiko Operational Risk of direct or indirect loss resulting form inadequate or failed internal processes, people, and systems or from external events. Some include reputational and strategic risk Technological innovation has seen rapid growth Automated clearing houses CHIPS

8. Risiko Operational Risk that technology investment fails to produce anticipated cost savings. Risk that technology may break down. Economies of scale. Economies of scope.

9. Risiko Nilai Tukar Valas Note that hedging foreign exposure by matching foreign assets and liabilities requires matching the maturities as well*. Otherwise, exposure to foreign interest rate risk is created.

10. Risiko Politik (atau Country or Sovereign risk) Result of exposure to foreign government which may impose restrictions on repayments to foreigners. Lack usual recourse via court system. Examples: South Korea, Indonesia, Thailand. More recently, Argentina.

11. Risiko Kredit Risk that promised cash flows are not paid in full. Firm specific credit risk Systematic credit risk High rate of charge-offs of credit card debt in the 80s and 90s Obvious need for credit screening and monitoring Diversification of credit risk

12. Risiko Likuiditas Risk of being forced to borrow, or sell assets in a very short period of time. Low prices result. May generate runs. Runs may turn liquidity problem into solvency problem. Risk of systematic bank panics.

13. Risiko Kecukupan Modal (Insolvency Risk) Risk of insufficient capital to offset sudden decline in value of assets to liabilities. Continental Illinois National Bank and Trust Original cause may be excessive interest rate, market, credit, off-balance-sheet, technological, FX, sovereign, and liquidity risks.

14. Interest Risks Budi Purwanto

15. Overview This chapter discusses the interest rate risk associated with financial intermediation: Federal Reserve policy Repricing model Maturity model Duration model *Term structure of interest rate risk *Theories of term structure of interest rates

16. Central Bank Policy and Interest Rate Risk Japan: March 2001 announced it would no longer target the uncollateralized overnight call rate. New target: Outstanding current account balances at BOJ Targeting of bank reserves in U.S. proved disastrous

17. Central Bank and Interest Rate Risk Effects of interest rate targeting. Lessens interest rate risk October 1979 to October 1982, nonborrowed reserves target regime. Implications of return to reserves target policy: Increases importance of measuring and managing interest rate risk.

18. Repricing Model Repricing or funding gap model based on book value. Contrasts with market value-based maturity and duration models recommended by the Bank for International Settlements (BIS). Rate sensitivity means time to repricing. Repricing gap is the difference between the rate sensitivity of each asset and the rate sensitivity of each liability: RSA - RSL.

19. Maturity Buckets Commercial banks must report repricing gaps for assets and liabilities with maturities of: One day. More than one day to three months. More than 3 three months to six months. More than six months to twelve months. More than one year to five years. Over five years.

20. Repricing Gap Example Assets Liabilities Gap Cum. Gap 1-day $ 20 $ 30 $-10 $-10 >1day-3mos. 30 40 -10 -20 >3mos.-6mos. 70 85 -15 -35 >6mos.-12mos. 90 70 +20 -15 >1yr.-5yrs. 40 30 +10 -5 >5 years 10 5 +5 0

21. Applying the Repricing Model DNIIi = (GAPi) DRi = (RSAi - RSLi) Dri Example: In the one day bucket, gap is -$10 million. If rates rise by 1%, DNIIi = (-$10 million) � .01 = -$100,000.

22. Applying the Repricing Model Example II: If we consider the cumulative 1-year gap, DNIIi = (CGAPi) DRi = (-$15 million)(.01) = -$150,000.

23. Rate-Sensitive Assets Examples from hypothetical balance sheet: Short-term consumer loans. If repriced at year-end, would just make one-year cutoff. Three-month T-bills repriced on maturity every 3 months. Six-month T-notes repriced on maturity every 6 months. 30-year floating-rate mortgages repriced (rate reset) every 9 months.

24. Rate-Sensitive Liabilities RSLs bucketed in same manner as RSAs. Demand deposits and passbook savings accounts warrant special mention. Generally considered rate-insensitive (act as core deposits), but there are arguments for their inclusion as rate-sensitive liabilities.

25. CGAP Ratio May be useful to express CGAP in ratio form as, CGAP/Assets. Provides direction of exposure and Scale of the exposure. Example: CGAP/A = $15 million / $270 million = 0.56, or 5.6 percent.

26. Equal Changes in Rates on RSAs and RSLs Example: Suppose rates rise 2% for RSAs and RSLs. Expected annual change in NII, ?NII = CGAP � ? R = $15 million � .01 = $150,000 With positive CGAP, rates and NII move in the same direction.

27. Unequal Changes in Rates If changes in rates on RSAs and RSLs are not equal, the spread changes. In this case, ?NII = (RSA � ? RRSA ) - (RSL � ? RRSL )

28. Unequal Rate Change Example Spread effect example: RSA rate rises by 1.2% and RSL rate rises by 1.0% ?NII = ? interest revenue - ? interest expense = ($155 million � 1.2%) - ($155 million � 1.0%) = $310,000

29. Restructuring Assets and Liabilities The FI can restructure its assets and liabilities, on or off the balance sheet, to benefit from projected interest rate changes. Positive gap: increase in rates increases NII Negative gap: decrease in rates increases NII

30. Weaknesses of Repricing Model Weaknesses: Ignores market value effects and off-balance sheet cash flows Overaggregative Distribution of assets & liabilities within individual buckets is not considered. Mismatches within buckets can be substantial. Ignores effects of runoffs Bank continuously originates and retires consumer and mortgage loans. Runoffs may be rate-sensitive.

31. The Maturity Model Explicitly incorporates market value effects. For fixed-income assets and liabilities: Rise (fall) in interest rates leads to fall (rise) in market price. The longer the maturity, the greater the effect of interest rate changes on market price. Fall in value of longer-term securities increases at diminishing rate for given increase in interest rates.

32. Maturity of Portfolio Maturity of portfolio of assets (liabilities) equals weighted average of maturities of individual components of the portfolio. Principles stated on previous slide apply to portfolio as well as to individual assets or liabilities. Typically, MA - ML > 0 for most banks and thrifts.

33. Effects of Interest Rate Changes Size of the gap determines the size of interest rate change that would drive net worth to zero. Immunization and effect of setting MA - ML = 0.

34. Maturity Matching and Interest Rate Exposure If MA - ML = 0, is the FI immunized? Extreme example: Suppose liabilities consist of 1-year zero coupon bond with face value $100. Assets consist of 1-year loan, which pays back $99.99 shortly after origination, and 1� at the end of the year. Both have maturities of 1 year. Not immunized, although maturities are equal. Reason: Differences in duration.

35. Duration The average life of an asset or liability The weighted-average time to maturity using present value of the cash flows, relative to the total present value of the asset or liability as weights.

36. *Term Structure of Interest Rates YTM

37. *Unbiased Expectations Theory Yield curve reflects market�s expectations of future short-term rates. Long-term rates are geometric average of current and expected short-term rates. _ _ ~ ~ RN = [(1+R1)(1+E(r2))�(1+E(rN))]1/N - 1

38. *Liquidity Premium Theory Allows for future uncertainty. Premium required to hold long-term. Investors have specific needs in terms of maturity. Yield curve reflects intersection of demand and supply of individual maturities.

39. Market Value-Based This chapter discusses a market value-based model for assessing and managing interest rate risk: Duration Computation of duration Economic interpretation Immunization using duration * Problems in applying duration

40. Price Sensitivity and Maturity In general, the longer the term to maturity, the greater the sensitivity to interest rate changes. Example: Suppose the zero coupon yield curve is flat at 12%. Bond A pays $1762.34 in five years. Bond B pays $3105.85 in ten years, and both are currently priced at $1000.

41. Example continued... Bond A: P = $1000 = $1762.34/(1.12)5 Bond B: P = $1000 = $3105.84/(1.12)10 Now suppose the interest rate increases by 1%. Bond A: P = $1762.34/(1.13)5 = $956.53 Bond B: P = $3105.84/(1.13)10 = $914.94 The longer maturity bond has the greater drop in price because the payment is discounted a greater number of times.

42. Coupon Effect Bonds with identical maturities will respond differently to interest rate changes when the coupons differ. This is more readily understood by recognizing that coupon bonds consist of a bundle of �zero-coupon� bonds. With higher coupons, more of the bond�s value is generated by cash flows which take place sooner in time. Consequently, less sensitive to changes in R.

43. Price Sensitivity of 6% Coupon Bond

44. Price Sensitivity of 8% Coupon Bond

45. Remarks on Preceding Slides The longer maturity bonds experience greater price changes in response to any change in the discount rate. The range of prices is greater when the coupon is lower. The 6% bond shows greater changes in price in response to a 2% change than the 8% bond. The first bond is has greater interest rate risk.

46. Duration Duration Weighted average time to maturity using the relative present values of the cash flows as weights. Combines the effects of differences in coupon rates and differences in maturity. Based on elasticity of bond price with respect to interest rate.

47. Duration Duration D = Snt=1[Ct� t/(1+r)t]/ Snt=1 [Ct/(1+r)t] Where D = duration t = number of periods in the future Ct = cash flow to be delivered in t periods n= term-to-maturity & r = yield to maturity (per period basis).

48. Duration Since the price of the bond must equal the present value of all its cash flows, we can state the duration formula another way: D = Snt=1[t ? (Present Value of Ct/Price)] Notice that the weights correspond to the relative present values of the cash flows.

49. Duration of Zero-coupon Bond For a zero coupon bond, duration equals maturity since 100% of its present value is generated by the payment of the face value, at maturity. For all other bonds: duration < maturity

50. Computing duration Consider a 2-year, 8% coupon bond, with a face value of $1,000 and yield-to-maturity of 12%. Coupons are paid semi-annually. Therefore, each coupon payment is $40 and the per period YTM is (1/2) � 12% = 6%. Present value of each cash flow equals CFt � (1+ 0.06)t where t is the period number.

51. Duration of 2-year, 8% bond: Face value = $1,000, YTM = 12%

52. Special Case Maturity of a consol: M = ?. Duration of a consol: D = 1 + 1/R

53. Duration Gap Suppose the bond in the previous example is the only loan asset (L) of an FI, funded by a 2-year certificate of deposit (D). Maturity gap: ML - MD = 2 -2 = 0 Duration Gap: DL - DD = 1.885 - 2.0 = -0.115 Deposit has greater interest rate sensitivity than the loan, so DGAP is negative. FI exposed to rising interest rates.

54. Features of Duration Duration and maturity: D increases with M, but at a decreasing rate. Duration and yield-to-maturity: D decreases as yield increases. Duration and coupon interest: D decreases as coupon increases

55. Economic Interpretation Duration is a measure of interest rate sensitivity or elasticity of a liability or asset: [dP/P] ? [dR/(1+R)] = -D Or equivalently, dP/P = -D[dR/(1+R)] = -MD � dR where MD is modified duration.

56. Economic Interpretation To estimate the change in price, we can rewrite this as: dP = -D[dR/(1+R)]P = -(MD) � (dR) � (P) Note the direct linear relationship between dP and -D.

57. Semi-annual Coupon Payments With semi-annual coupon payments: (dP/P)/(dR/R) = -D[dR/(1+(R/2)]

58. An example: Consider three loan plans, all of which have maturities of 2 years. The loan amount is $1,000 and the current interest rate is 3%. Loan #1, is an installment loan with two equal payments of $522.61. Loan #2 is a discount loan, which has a single payment of $1,060.90. Loan #3 is structured as a 3% annual coupon bond.

59. Duration as Index of Interest Rate Risk

60. Immunizing theBalance Sheet of an FI Duration Gap: From the balance sheet, E=A-L. Therefore, DE=DA-DL. In the same manner used to determine the change in bond prices, we can find the change in value of equity using duration. DE = [-DAA + DLL] DR/(1+R) or DE = -[DA - DLk]A(DR/(1+R))

61. Duration and Immunizing The formula shows 3 effects: Leverage adjusted D-Gap The size of the FI The size of the interest rate shock

62. An example: Suppose DA = 5 years, DL = 3 years and rates are expected to rise from 10% to 11%. (Rates change by 1%). Also, A = 100, L = 90 and E = 10. Find change in E. DE = -[DA - DLk]A[DR/(1+R)] = -[5 - 3(90/100)]100[.01/1.1] = - $2.09. Methods of immunizing balance sheet. Adjust DA , DL or k.

63. Immunization and Regulatory Concerns Regulators set target ratios for a bank�s capital (net worth): Capital (Net worth) ratio = E/A If target is to set ?(E/A) = 0: DA = DL But, to set ?E = 0: DA = kDL

64. *Limitations of Duration Immunizing the entire balance sheet need not be costly. Duration can be employed in combination with hedge positions to immunize. Immunization is a dynamic process since duration depends on instantaneous R. Large interest rate change effects not accurately captured. Convexity More complex if nonparallel shift in yield curve.

65. *Convexity The duration measure is a linear approximation of a non-linear function. If there are large changes in R, the approximation is much less accurate. All fixed-income securities are convex. Convexity is desirable, but greater convexity causes larger errors in the duration-based estimate of price changes.

66. *Convexity Recall that duration involves only the first derivative of the price function. We can improve on the estimate using a Taylor expansion. In practice, the expansion rarely goes beyond second order (using the second derivative).

67. *Modified duration DP/P = -D[DR/(1+R)] + (1/2) CX (DR)2 or DP/P = -MD DR + (1/2) CX (DR)2 Where MD implies modified duration and CX is a measure of the curvature effect. CX = Scaling factor � [capital loss from 1bp rise in yield + capital gain from 1bp fall in yield] Commonly used scaling factor is 108.

68. *Calculation of CX Example: convexity of 8% coupon, 8% yield, six-year maturity Eurobond priced at $1,000. CX = 108[DP-/P + DP+/P] = 108[(999.53785-1,000)/1,000 + (1,000.46243-1,000)/1,000)] = 28.

69. *Duration Measure: Other Issues Default risk Floating-rate loans and bonds Duration of demand deposits and passbook savings Mortgage-backed securities and mortgages Duration relationship affected by call or prepayment provisions.

70. *Contingent Claims Interest rate changes also affect value of off-balance sheet claims. Duration gap hedging strategy must include the effects on off-balance sheet items such as futures, options, swaps, caps, and other contingent claims.

71. Risiko Nilai Tukar Budi Purwanto

72. Forex Risks This chapter discusses foreign exchange risk to which FIs are exposed. This issue has become increasingly important for FIs due to hedging needs and speculative positions taken to increase income.

73. Background Globalization of financial markets has increased foreign exposure of most FIs. FI may have assets or liabilities denominated in foreign currency (in addition to direct positions in foreign currency). Foreign currency holdings exceed direct portfolio investments.

74. Sources of FX Risk Spot positions denominated in foreign currency Forward positions denominated in foreign currency Net exposure = (FX assets - FX liab.) + (FX bought - FX sold)

75. FX Risk Exposure FI may have positions in spot and forward markets. Could match foreign currency assets and liabilities to hedge F/X risk Must also hedge against foreign interest rate risk (by matching durations, for example)

76. Trends in FX Value of foreign positions has increased Volume of foreign currency trading has decreased Causes: Investment bank mergers Increased trading efficiency through technological innovation Introduction of the euro

77. FX Risk Exposure Greater exposure to a foreign currency combined with greater volatility of the foreign currency implies greater DEAR. Dollar loss/gain in currency i = [Net exposure in foreign currency i measured in U.S. $] � Shock (Volatility) to the $/Foreign currency i exchange rate

78. FX Trading FX markets turnover often greater than $1.8 trillion per day. The market moves between Tokyo, NYC and London over the day allowing for what is essentially a 24-hour market. Overnight exposure adds to the risk.

79. Trading Activities Basically 4 trading activities: Purchase and sale of currencies to complete international transactions. Facilitating positions in foreign real and financial investments. Accommodating hedging activities Speculation.

80. Profitability of FX Trading For large US banks, trading income is a major source of income. Volatility of European currencies are declining (due to euro) Volatility in Asian and emerging markets currencies higher Risk arises from taking open positions in currencies

81. Foreign Assets & Liabilities Mismatches between foreign asset and liability portfolios Ability to raise funds from internationally diverse sources presents opportunities as well as risks Greater competition in well-developed (lower risk) markets

82. Return and Risk of Foreign Investments Returns are affected by: Spread between costs and revenues changes in FX rates Changes in FX rates are not under the control of the FI

83. Risk and Hedging Hedge can be constructed on balance sheet or off balance sheet. On - balance-sheet hedge will also require duration matching to control exposure to foreign interest rate risk. Off-balance-sheet hedge using forwards, futures, or options.

84. Interest Rate Parity Theorem Equilibrium condition is that there should be no arbitrage opportunities available through lending and borrowing across currencies. This requires that 1+r(domestic) = (F/S)[1+r (foreign)] Difference in interest rates will be offset by the expected change in exchange rates.

85. Multicurrency Positions Since the banks generally take positions in more than one currency simultaneously, their risk is partially reduced through diversification. Overall, world bond markets are significantly, but not fully integrated which leaves open the opportunity to reduce exposure by diversifying.

86. Diversification Effects (continued) High correlations between the bond returns may be due to high correlation of real interest rates over time and/or inflation expectations. ri = rri + iei Nominal return = real return + E[inflation]

87. Credit Risks Budi Purwanto

88. Overview This chapter discusses types of loans, and the analysis and measurement of credit risk on individual loans. This is important for purposes of: Pricing loans and bonds Setting limits on credit risk exposure

89. Credit Quality Problems Problems with junk bonds, LDC loans, residential and farm mortgage loans. More recently, credit card loans and auto loans. Crises in Asian countries such as Korea, Indonesia, Thailand, and Malaysia.

90. Credit Quality Problems Over the 90s, improvements in NPLs for large banks and overall credit quality. Recent exposure to borrowers such as Enron. New types of credit risk related to loan guarantees and off-balance-sheet activities. Increased emphasis on credit risk evaluation.

91. Types of Loans: C&I loans: secured and unsecured Spot loans, Loan commitments Decline in C&I loans originated by commercial banks and growth in commercial paper market. RE loans: primarily mortgages Fixed-rate, ARM Mortgages can be subject to default risk when loan-to-value declines.

92. Consumer loans Individual (consumer) loans: personal, auto, credit card. Nonrevolving loans Automobile, mobile home, personal loans Growth in credit card debt Visa, MasterCard Proprietary cards such as Sears, AT&T Risks affected by competitive conditions and usury ceilings

93. Other loans Other loans include: Farm loans Other banks Nonbank FIs Broker margin loans Foreign banks and sovereign governments State and local governments

94. Return on a Loan: Factors: interest payments, fees, credit risk premium, collateral, other requirements such as compensating balances and reserve requirements. Return = inflow/outflow k = (f + (L + M ))/(1-[b(1-R)]) Expected return: E(r) = p(1+k)

95. Lending Rates and Rationing At retail: Usually a simple accept/reject decision rather than adjustments to the rate. Credit rationing. If accepted, customers sorted by loan quantity. At wholesale: Use both quantity and pricing adjustments.

96. Measuring Credit Risk Qualitative models: borrower specific factors are considered as well as market or systematic factors. Specific factors include: reputation, leverage, volatility of earnings, covenants and collateral. Market specific factors include: business cycle and interest rate levels.

97. Credit Scoring Models Linear probability models: Zi = Statistically unsound since the Z�s obtained are not probabilities at all. *Since superior statistical techniques are readily available, little justification for employing linear probability models.

98. Other Credit Scoring Models Logit models: overcome weakness of the linear probability models using a transformation (logistic function) that restricts the probabilities to the zero-one interval. Other alternatives include Probit and other variants with nonlinear indicator functions.

99. Altman�s Linear Discriminant Model: Z=1.2X1+ 1.4X2 +3.3X3 + 0.6X4 + 1.0X5 Critical value of Z = 1.81. X1 = Working capital/total assets. X2 = Retained earnings/total assets. X3 = EBIT/total assets. X4 = Market value equity/ book value LT debt. X5 = Sales/total assets.

100. Linear Discriminant Model Problems: Only considers two extreme cases (default/no default). Weights need not be stationary over time. Ignores hard to quantify factors including business cycle effects. Database of defaulted loans is not available to benchmark the model.

101. Term Structure Based Methods If we know the risk premium we can infer the probability of default. Expected return equals risk free rate after accounting for probability of default. p (1+ k) = 1+ i May be generalized to loans with any maturity or to adjust for varying default recovery rates. The loan can be assessed using the inferred probabilities from comparable quality bonds.

102. Mortality Rate Models Similar to the process employed by insurance companies to price policies. The probability of default is estimated from past data on defaults. Marginal Mortality Rates: MMR1 = (Value Grade B default in year 1) (Value Grade B outstanding yr.1) MMR2 = (Value Grade B default in year 2) (Value Grade B outstanding yr.2)

103. RAROC Models Risk adjusted return on capital. This is one of the more widely used models. Incorporates duration approach to estimate worst case loss in value of the loan: DL = -DL x L x (DR/(1+R)) where DR is an estimate of the worst change in credit risk premiums for the loan class over the past year. RAROC = one-year income on loan/DL

104. Option Models: Employ option pricing methods to evaluate the option to default. Used by many of the largest banks to monitor credit risk. KMV Corporation markets this model quite widely.

105. Applying Option Valuation Model Merton showed value of a risky loan F(t) = Be-it[(1/d)N(h1) +N(h2)] Written as a yield spread k(t) - i = (-1/t)ln[N(h2) +(1/d)N(h1)] where k(t) = Required yield on risky debt ln = Natural logarithm i = Risk-free rate on debt of equivalent maturity.

106. *CreditMetrics �If next year is a bad year, how much will I lose on my loans and loan portfolio?� VAR = P � 1.65 � s Neither P, nor s observed. Calculated using: (i)Data on borrower�s credit rating; (ii) Rating transition matrix; (iii) Recovery rates on defaulted loans; (iv) Yield spreads.

107. * Credit Risk+ Developed by Credit Suisse Financial Products. Based on insurance literature: Losses reflect frequency of event and severity of loss. Loan default is random. Loan default probabilities are independent. Appropriate for large portfolios of small loans. Modeled by a Poisson distribution.

108. Loan Portfolio Risks This chapter discusses the management of credit risk in a loan (asset) portfolio context. It also discusses the setting of credit exposure limits to industrial sectors and regulatory approaches to monitoring credit risk. The National Association of Insurance Commissioners has also developed limits for different types of assets and borrowers in insurers� portfolios.

109. Simple Models of Loan Concentration Migration analysis Track credit rating changes within sector or pool of loans. Rating transition matrix.

110. Rating Transition Matrix Risk grade: end of year 1 2 3 Default Risk grade: 1| .85 .10 .04 .01 beginning 2| .12 .83 .03 .02 of year 3| .03 .13 .80 .04

111. Simple Models of Loan Concentration Concentration limits On loans to individual borrower. Concentration limit = Maximum loss ? Loss rate. Maximum loss expressed as percent of capital.

112. Diversification and Modern Portfolio Theory Applying portfolio theory to loans Using loans to construct the efficient frontier. Minimum risk portfolio. Low risk Low return.

113. Applying Portfolio Theory to Loans Require (i) expected return on loan(measured by all-in-spread); (ii) loan risk; (iii) correlation of loan default risks.

114. Modern Portfolio Theory

115. KMV Portfolio Manager Model Ri = AISi - E(Li) = AISi - [EDFi � LGDi] si = ULi = si � LGDi = [EDFi(1-EDFi)]� � LGDi rij = correlation between systematic return components of equity returns of borrower i and borrower j.

116. Partial Applications of Portfolio Theory Loan volume-based models Commercial bank call reports Can be aggregated to estimate national allocations. Shared national credit National database that breaks commercial and industrial loan volume into 2-digit SIC codes.

117. Partial Applications Loan volume-based models (continued) Provide market benchmarks. Standard deviation measure of loan allocation deviation.

118. Loan Loss Ratio-Based Models Estimate loan loss risk by SIC sector. Time-series regression: [sectoral losses in ith sector] [ loans to ith sector ] = a + bi [total loan losses] [ total loans ]

119. Regulatory Models Credit concentration risk evaluation largely subjective. Life and PC insurance regulators propose limits on investments in securities or obligations of any single issuer. Diversification limits.

120. Market Risks Budi Purwanto

121. Overview This chapter discusses the nature of market risk and appropriate measures Dollar exposure RiskMetrics Historic or back simulation Monte Carlo simulation Links between market risk and capital requirements

122. Market Risk: Market risk is the uncertainty resulting from changes in market prices . It can be measured over periods as short as one day. Usually measured in terms of dollar exposure amount or as a relative amount against some benchmark.

123. Market Risk Measurement Important in terms of: Management information Setting limits Resource allocation (risk/return tradeoff) Performance evaluation Regulation

124. Calculating Market Risk Exposure Generally concerned with estimated potential loss under adverse circumstances. Three major approaches of measurement JPM RiskMetrics (or variance/covariance approach) Historic or Back Simulation Monte Carlo Simulation

125. JP Morgan RiskMetrics Model Idea is to determine the daily earnings at risk = dollar value of position � price sensitivity � potential adverse move in yield or, DEAR = Dollar market value of position � Price volatility. Can be stated as (-MD) � adverse daily yield move where, MD = D/(1+R) Modified duration = MacAulay duration/(1+R)

126. Confidence Intervals If we assume that changes in the yield are normally distributed, we can construct confidence intervals around the projected DEAR. (Other distributions can be accommodated but normal is generally sufficient). Assuming normality, 90% of the time the disturbance will be within 1.65 standard deviations of the mean.

127. Confidence Intervals: Example Suppose that we are long in 7-year zero-coupon bonds and we define �bad� yield changes such that there is only 5% chance of the yield change being exceeded in either direction. Assuming normality, 90% of the time yield changes will be within 1.65 standard deviations of the mean. If the standard deviation is 10 basis points, this corresponds to 16.5 basis points. Concern is that yields will rise. Probability of yield increases greater than 16.5 basis points is 5%.

128. Confidence Intervals: Example Price volatility = (-MD) ? (Potential adverse change in yield) = (-6.527) ? (0.00165) = -1.077% DEAR = Market value of position ? (Price volatility) = ($1,000,000) ? (.01077) = $10,770

129. Confidence Intervals: Example To calculate the potential loss for more than one day: Market value at risk (VAR) = DEAR � ?N Example: For a five-day period, VAR = $10,770 � ?5 = $24,082

130. Foreign Exchange & Equities In the case of Foreign Exchange, DEAR is computed in the same fashion we employed for interest rate risk. For equities, if the portfolio is well diversified then DEAR = dollar value of position � stock market return volatility where the market return volatility is taken as 1.65 sM.

131. Aggregating DEAR Estimates Cannot simply sum up individual DEARs. In order to aggregate the DEARs from individual exposures we require the correlation matrix. Three-asset case: DEAR portfolio = [DEARa2 + DEARb2 + DEARc2 + 2rab � DEARa � DEARb + 2rac � DEARa � DEARc + 2rbc � DEARb � DEARc]1/2

132. Historic or Back Simulation Advantages: Simplicity Does not require normal distribution of returns (which is a critical assumption for RiskMetrics) Does not need correlations or standard deviations of individual asset returns.

133. Historic or Back Simulation Basic idea: Revalue portfolio based on actual prices (returns) on the assets that existed yesterday, the day before, etc. (usually previous 500 days). Then calculate 5% worst-case (25th lowest value of 500 days) outcomes. Only 5% of the outcomes were lower.

134. Estimation of VAR: Example Convert today�s FX positions into dollar equivalents at today�s FX rates. Measure sensitivity of each position Calculate its delta. Measure risk Actual percentage changes in FX rates for each of past 500 days. Rank days by risk from worst to best.

135. Weaknesses Disadvantage: 500 observations is not very many from statistical standpoint. Increasing number of observations by going back further in time is not desirable. Could weight recent observations more heavily and go further back.

136. Monte Carlo Simulation To overcome problem of limited number of observations, synthesize additional observations. Perhaps 10,000 real and synthetic observations. Employ historic covariance matrix and random number generator to synthesize observations. Objective is to replicate the distribution of observed outcomes with synthetic data.

137. Regulatory Models BIS (including Federal Reserve) approach: Market risk may be calculated using standard BIS model. Specific risk charge. General market risk charge. Offsets. Subject to regulatory permission, large banks may be allowed to use their internal models as the basis for determining capital requirements.

138. BIS Model Specific risk charge: Risk weights � absolute dollar values of long and short positions General market risk charge: reflect modified durations ? expected interest rate shocks for each maturity Vertical offsets: Adjust for basis risk Horizontal offsets within/between time zones

139. Large Banks: BIS versus RiskMetrics In calculating DEAR, adverse change in rates defined as 99th percentile (rather than 95th under RiskMetrics) Minimum holding period is 10 days (means that RiskMetrics� daily DEAR multiplied by ?10. Capital charge will be higher of: Previous day�s VAR (or DEAR ? ?10) Average Daily VAR over previous 60 days times a multiplication factor ? 3.

140. Operational Risks Budi Purwanto

141. Overview This chapter discusses the factors affecting operational returns and risks, and the importance of optimal management and control of labor, capital, and other input sources and their costs. The emphasis is on technology and its impact on risk and return. Examples: Risks resulting from innovations in IT, and effects of terrorist attacks on key technologies.

142. Sources of Operational Risk Technology Employees Customer relationships Capital assets External

143. Importance of Technology Efficient technological base can result in: Lower costs Through improved allocation of inputs. Increased revenues Through wider range of outputs. Earnings before taxes = (Interest income - Interest expense) + (Other income - Noninterest expense) - Provision for loan losses

144. Impact of Technology Interest income can be increased Through wider array of outputs or cross selling. Interest expense can be decreased Through improved access to markets for liabilities Fedwire, CHIPS

145. Impact of Technology Other income can be increased Through electronic handling of fee generating OBS activities such as LCs and derivatives Noninterest expenses can be reduced Through improved efficiency of back office operations using technology. Especially true for securities-related activities.

146. Impact on Wholesale Banking Improvements to cash management Controlled disbursement accounts Account reconciliation Wholesale lockbox Electronic lockbox Funds concentration

147. Impact on Wholesale Banking (continued) Electronic funds transfer Check deposit services Electronic initiation of letters of credit Treasury management software Electronic data interchange Facilitating B2B e-commerce Electronic billing

148. Impact on Wholesale Banking (continued) Verifying identities Issue of law enforcement access to encrypted data since September 11, 2001 Assisting small business entry into e-commerce

149. Impact on Retail Banking Automated teller machines Point-of-sale debit cards Home banking Preauthorized debits/credits Pay-by-phone E-mail billing Online banking Smart cards

150. Effects of Technology on Revenues and Costs Investments in technology are risky Potentially negative NPV projects due to uncertainty and potential competitive responses Potential agency conflicts: Growth-oriented investments may not maximize shareholder�s value Losses on technological investments can weaken an FI

151. Effects of Technology on Revenues and Costs Evidence shows the impact of regulation on value of technological innovations. Branching restrictions in U.S. affect the value of cash management services, for example. Less valuable in Europe where comparable restrictions are absent

152. Effects of Technology on Revenues and Costs Revenue effects: Facilitates cross-marketing Increases innovation Service quality effects Survival of small banks and value of �human touch� Cost effects: Technological improvements Shift in cost curve.

153. Effects on Costs (continued) Economies of scale Optimal size depends on shape of average cost curve.

154. Effects on Costs (continued) Economies of scope Multiple outputs may provide synergies in production. Diseconomies of scope Specialization may have cost benefits in production and delivery of some FI services

155. Testing for Economies of Scale and Scope Production approach: Views FI as producing output of services using inputs of labor and capital. C = f(y,w,r) Intermediation Approach: Includes funds used to produce intermediated services among the inputs. C = f(y,w,r, k)

156. Empirical Findings Evidence economies of scale for banks up to the $10 billion to $25 billion range. X-inefficiencies may be more important. Inconclusive evidence on scope. Recent studies using a profit-based approach find that large FIs tend to be more efficient in revenue generation.

157. Technology and Evolution of the Payments System Use of electronic transactions higher in other countries. (E.g., TARGET). U.S. Payments system: FedWire Clearing House Interbank Payments System (CHIPS) Combined value of transactions often more than $2.7 trillion per day.

158. Wire Transfer System Risks Daylight overdraft risk FedWire settlement at 6:30 EST Example of magnitude of daylight overdraft risk: Bank of New York (BONY) Regulation J guarantees payment finality of wire transfer messages by the Fed Regulation F sets exposure limits to individual correspondent banks.

159. Risks (continued) International Technology Transfer Risk Crime and Fraud Risk Regulatory Risk Technology facilitates avoidance of regulation by locating in least regulated state or country. Tax Avoidance Competition Risk

160. Other Operational Risks Employees Turnover Key personnel Fraud Errors Rogue trading (Barings, Allied Irish/Allfirst) Money laundering Confidentiality breach

161. Technology Risks Programming error Model risk Mark-to-market error Management information IT/Telecomm systems outage Technology provider failure Contingency planning

162. Customer Relationship Risks Contractual disagreement Dissatisfaction from poorly performing technology Default

163. Capital Asset Risk Safety Security Operating costs Fire/flood

164. External risks External fraud Taxation risk Legal risk War Market collapse Reputation risk Relationship risk

165. Controlling Operational Risk Loss prevention: Training, development, review of employees Loss control: Planning, organization, back-up Loss financing: External insurance Loss insulation: FI capital

166. Optimal Risk Management

167. Regulatory Issues 1999 Basel Committee on Banking Supervision noted the importance of operational risks Required capital: Basic Indicator Approach Standardized Approach Internal Measurement Approach Consumer protection issues

168. Liquidity Risk Budi Purwanto

169. Overview This chapter explores the problem of liquidity risk faced to a greater or lesser extent by all FIs. Methods of measuring liquidity risk, and its consequences are discussed. The chapter also discusses the regulatory mechanisms put in place to control liquidity risk.

170. Causes of Liquidity Risk Asset side May be forced to liquidate assets too rapidly May result from loan commitments Traditional approach: reserve asset management. Alternative: liability management.

171. Causes of Liquidity Risk Liability side Reliance on demand deposits Core deposits Need to be able to predict the distribution of net deposit drains. Managed by: purchased liquidity management stored liquidity management

172. Liability Management Purchased liquidity Federal funds market or repo market. Managing the liability side preserves asset side of balance sheet. Borrowed funds likely at higher rates than interest paid on deposits. Deposits are insured Regulatory concerns: growth of wholesale funds

173. Liability Management Alternative: Stored Liquidity Management Liquidate assets. In absence of reserve requirements, banks tend to hold reserves. E.g. In U.K. reserves ~ 1% or more. Downside: opportunity cost of reserves. Decreases size of balance sheet Requires holding excess noninterest-bearing assets Combine purchased and stored liquidity management

174. Asset Side Liquidity Risk Risk from loan commitments and other credit lines: met either by borrowing funds or by running down reserves Current levels of loan commitments are dangerously high according to regulators

175. Measuring Liquidity Exposure Net liquidity statement: shows sources and uses of liquidity. Sources: (i) Cash type assets, (ii) maximum amount of borrowed funds available, (iii) excess cash reserves Uses include: borrowed or money market funds already utilized, and any amounts already borrowed from the Fed.

176. Other Measures: Peer group comparisons: usual ratios include borrowed funds/total assets, loan commitments/assets etc. Liquidity index: weighted sum of �fire sale price� P to fair market price, P*, where the portfolio weights are the percent of the portfolio value formed by the individual assets. I = S wi(Pi /Pi*)

177. Measuring Liquidity Risk Financing gap and the financing requirement: Financing gap = Average loans - Average deposits or, financing gap + liquid assets = financing requirement. The gap can be used in peer group comparisons or examined for trends within an individual FI. Example of excessive financing requirement: Continental Illinois, 1984.

178. BIS Approach: Maturity ladder/Scenario Analysis For each maturity, assess all cash inflows versus outflows Daily and cumulative net funding requirements can be determined in this manner Must also evaluate �what if� scenarios in this framework

179. Liquidity Planning Important to know which types of depositors are likely to withdraw first in a crisis. Composition of the depositor base will affect the severity of funding shortfalls. Allow for seasonal effects. Delineate managerial responsibilities clearly.

180. Bank Runs Can arise due to concern about bank�s solvency. Failure of a related FI. Sudden changes in investor preferences. Demand deposits are first come first served. Depositor�s place in line matters. Bank panic: systemic or contagious bank run.

181. Alleviating Bank Runs: Regulatory measures to reduce likelihood of bank runs: FDIC Discount window Not without economic costs.

182. Liquidity Risk for Other FIs Life Cos. Hold reserves to offset policy cancellations. The pattern is normally predictable. An example: First Capital in California, 1991. CA regulators placed limits on ability to surrender policies. Problem is less severe for P&C insurers since assets tend to be shorter term and more liquid.

183. Mutual Funds Net asset value (NAV) of the fund is market value. The incentive for runs is not like the situation faced by banks. Asset losses will be shared on a pro rata basis so there is no advantage to being first in line.

184. Liability and Liquidity Depository institutions and life insurance companies are highly exposed to liquidity risk. This chapter discusses how these firms can control liquidity risk, the motives for holding liquid assets, and specific issues associated with liability and liquidity risk management.

185. Liquid Asset Management Examples: T-bills, T-notes, T-bonds Benefits of holding large quantities of liquid assets Costs of holding liquid assets

186. Liquid Asset Management Reasons for regulating minimum holdings of liquid assets: Monetary policy Taxation

187. Composition Composition of liquid asset portfolio Liquid assets ratio Cash and government securities in countries such as U.K. Similar case for U.S. life insurance companies (regulated at state level) U.S. banks: cash-based, but banks view government securities as buffer reserves.

188. Return-Risk Trade-off Cash immediacy versus reduced return Constrained optimization Privately optimal reserve holdings Regulator imposed reserve holdings

189. U.S. Cash Reserve Requirements Incremental reserve requirements for transaction accounts: First $5.5 million 0.0% $5.5 million to $42.8 million 3.0% $42.8 million + 10.0%

190. Reserve Management Problem Computation period runs from a Tuesday to a Monday, 14 days later. Average daily reserves are computed as a fraction of the average daily deposits over the period. This means that Friday deposit figures count 3 times in the average. �Weekend Game� Sweep accounts

191. Reserve Management The reserve maintenance period, differs from the computation period by 17 days. Lagged reserve accounting as of July 1998. Previously, contemporaneous (2-day lag). Benefits of lagged reserve accounting

192. Under-/Over-shooting Allowance for up to a 4% error in average daily reserves without penalty. Surplus reserves required for next 2-week period Undershooting by more than 4% penalized by a 2% markup on rate charged against shortfall. Frequent undershooting likely to attract scrutiny by regulators

193. Undershooting DI has two options near the end of the maintenance period Liquidate assets Borrow reserves fed funds repurchase agreements

194. Discount Window Reserve shortfalls in the past Discount window borrowing discount rate usually lower than market rates Risks of gaming the system

195. Overshooting First 4 percent can be carried forward to next period Excess reserves typically low due to opportunity costs Knife-Edge problem

196. Funding Risk versus Cost Funding Cost

197. Liability Management Note the tradeoff between funding risk and funding cost. Demand deposits are a source of cheap funds but there is high risk of withdrawal. NOW accounts: manager can adjust the explicit interest rate, implicit rate and minimum balance requirements to alter attractiveness of NOW deposits.

198. Deposit Accounts Passbook Savings Accounts: Not checkable. Bank also has power to delay withdrawals for as long as a month. Money market deposit accounts: Somewhat less liquid than demand deposits and NOW accounts. Impose minimum balance requirements and limit the number and denomination of checks each month.

199. Time Deposits and CDs Retail CDs: Face values under $100,000 and maturities from 2 weeks to 8 years. Penalties for early withdrawal. Unlike T-bills, interest earned on CDs is taxable. Wholesale CDs: Minimum denominations of $100,000. Wholesale CDs are negotiable.

200. Fed Funds Fed funds is the interbank market for excess reserves. 90% have maturities of 1 day. Fed funds rate can be highly variable Prior to July 1998: especially around the second Tuesday and Wednesday of each period. (as high as 30% and lows close to 0% on some Wednesdays). Rollover risk

201. Repurchase Agreements RPs are collateralized fed funds transactions. Usually backed by government securities. Can be more difficult to arrange than simple fed funds loans. Generally below fed funds rate

202. Other Borrowings Bankers acceptances Commercial paper Medium-term notes Discount window loans

203. Historical Notes Since 1960, ratio of liquid to illiquid assets has fallen from about 52% to about 26%. But, loans themselves have also become more liquid. Securitization of DI loans In the same period, there has been a shift away from sources of funds that have a high risk of withdrawal.

204. Historical Notes During the period since 1960: Noticeable differences between large and small banks with respect to use of low withdrawal risk funds. Reliance on borrowed funds does have its own risks as with Continental Illinois.

205. Liquidity Risk in Other FIs Insurance companies Diversify across contracts Hold marketable assets Securities firms Example: Drexel Burnham Lambert

206. Kepustakaan Siamat, Dahlan. 2004. Manajemen Lembaga Keuangan. Lembaga Penerbit Fakultas Ekonomi Universitas Indonesia. Saunders, A., Cornett M.M. 2006. Financial Institution Management. McGraw-Hill International. Kasmir. 2002. Manajemen Perbankan. Jakarta: Divisi Buku Perguruan Tinggi PT RajaGrafindo Persada. Kuncoro, M & Suhardjono. 2002. Manajemen Perbankan: Teori dan Aplikasi. BPFE Yogyakarta. Riyadi, S. 2004. Banking Assets Liability Management. Penerbitan FE-UI Gandapradja, P. 2004. Dasar dan Prinsip Pengawasan Bank. Penerbit PT Gramedia Utama.