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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).. Risiko Suku Bunga. Risiko suku b
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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)]½ × 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
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