Transcript Pengantar Manajemen Perbankan

Manajemen Risiko
Lembaga Keuangan
Budi Purwanto
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 bunga akibat intermediasi:
 Ketidakcocokan jatuh tempo aktiva dan
pasiva.
 Ketidakcocokan merupakan masalah
sistematis dalam intermediasi keuangan.
 Risiko pendanaan-kembali.
 Risiko investasi-kembali.
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.
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.
Risiko ekonomi makro
 Kenaikan inflasi atau volatilitasnya.
 Keduanya mempengaruhi suku bunga.
 Peningkatan pengangguran
 Mempengaruhi risiko kredit.
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
Risiko Operational
 Risk that technology investment fails to
produce anticipated cost savings.
 Risk that technology may break down.
 Economies of scale.
 Economies of scope.
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.
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.
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
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.
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.
Interest Risks
Budi Purwanto
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
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
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.
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.
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.
Repricing Gap Example
Assets
1-day
$ 20
>1day-3mos.
30
>3mos.-6mos. 70
>6mos.-12mos. 90
>1yr.-5yrs.
40
>5 years
10
Liabilities
$ 30
40
85
70
30
5
Gap Cum. Gap
$-10 $-10
-10
-20
-15
-35
+20
-15
+10
-5
+5
0
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.
Applying the Repricing
Model
 Example II:
If we consider the cumulative 1-year gap,
DNIIi = (CGAPi) DRi = (-$15 million)(.01)
= -$150,000.
Rate-Sensitive Assets
 Examples from hypothetical balance sheet:
 Short-term consumer loans. If repriced at yearend, 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.
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.
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.
Equal Changes in Rates
on RSAs and RSLs
 Example: Suppose rates rise 2% for
RSAs and RSLs. Expected annual
change in NII,
DNII = CGAP × D R
= $15 million × .01
= $150,000
With positive CGAP, rates and NII move in
the same direction.
Unequal Changes in Rates
 If changes in rates on RSAs and RSLs are
not equal, the spread changes. In this case,
DNII = (RSA × D RRSA ) - (RSL × D RRSL )
Unequal Rate Change
Example
 Spread effect example:
RSA rate rises by 1.2% and RSL rate rises by
1.0%
DNII = D interest revenue - D interest
expense
= ($155 million × 1.2%) - ($155 million ×
1.0%)
= $310,000
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
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.
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.
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.
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.
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.
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.
*Term Structure of
Interest Rates
YTM
YTM
Time to Maturity
Time to Maturity
Time to Maturity
Time to Maturity
*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
*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.
Market Value-Based
 This chapter discusses a market valuebased model for assessing and managing
interest rate risk:





Duration
Computation of duration
Economic interpretation
Immunization using duration
* Problems in applying duration
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.
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.
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.
Price Sensitivity of 6%
Coupon Bond
r
8%
6%
4%
Range
n
40
$802
$1,000
$1,273
$471
20
$864
$1,000
$1,163
$299
10
$919
$1,000
$1,089
$170
2
$981
$1,000
$1,019
$37
Price Sensitivity of
8% Coupon Bond
r
10%
8%
6%
Range
n
40
$828
$1,000
$1,231
$403
20
$875
$1,000
$1,149
$274
10
$923
$1,000
$1,085
$162
2
$981
$1,000
$1,019
$38
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.
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.
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).
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.
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
Computing duration
 Consider a 2-year, 8% coupon bond, with
a face value of $1,000 and yield-tomaturity of 12%. Coupons are paid semiannually.
 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
Duration of 2-year, 8% bond:
Face value = $1,000, YTM = 12%
t
years CFt
PV(CFt)
1
0.5
40
37.736
Weight
(x)
0.041
x × years
2
1.0
40
35.600
0.038
0.038
3
1.5
40
33.585
0.036
0.054
4
2.0
1,040 823.777
0.885
1.770
P = 930.698
1.000
D=1.883
(years)
0.020
Special Case
 Maturity of a consol: M = .
 Duration of a consol: D = 1 + 1/R
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.
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
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.
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.
Semi-annual Coupon
Payments
 With semi-annual coupon payments:
(dP/P)/(dR/R) = -D[dR/(1+(R/2)]
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.
Duration as Index of
Interest Rate Risk
Yield
Loan Value
2%
3%
dP n
D
Installment $1014.67 $1000 $28.98 2 1.493
Discount
$1019.70 $1000 $38.84 2 2.000
Coupon
$1019.41 $1000 $38.27 2 1.97
Immunizing the
Balance 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))
Duration and Immunizing
 The formula shows 3 effects:
 Leverage adjusted D-Gap
 The size of the FI
 The size of the interest rate shock
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.
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 D(E/A) = 0:
 DA = DL
 But, to set DE = 0:
 DA = kDL
*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.
*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.
*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).
*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.
*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.
*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.
*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.
Risiko Nilai Tukar
Budi Purwanto
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.
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.
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)
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)
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
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
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.
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.
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
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
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
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.
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.
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.
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]
Credit Risks
Budi Purwanto
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
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.
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.
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.
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
Other loans
 Other loans include:






Farm loans
Other banks
Nonbank FIs
Broker margin loans
Foreign banks and sovereign governments
State and local governments
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)
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.
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.
Credit Scoring Models
 Linear probability models:
n
Zi =
  j X i, j  error
j =1
 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.
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.
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.
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.
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.
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)
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
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.
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.
*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.
* 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.
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.
Simple Models of Loan
Concentration
 Migration analysis
 Track credit rating changes within sector or pool
of loans.
 Rating transition matrix.
Rating Transition Matrix
Risk grade:
beginning
of year
1|
2|
3|
Risk grade: end of year
1
2
3
Default
.85 .10 .04 .01
.12 .83 .03 .02
.03 .13 .80 .04
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.
Diversification and Modern
Portfolio Theory
 Applying portfolio theory to loans
 Using loans to construct the efficient frontier.
 Minimum risk portfolio.
 Low risk
 Low return.
Applying Portfolio Theory to
Loans
 Require
 (i) expected return on loan(measured by allin-spread);
 (ii) loan risk;
 (iii) correlation of loan default risks.
Modern Portfolio Theory
n
R p =  X i Ri
i =1
2
sp
n
=
i =1
2 2
Xi si
n n
   X i X js i , j
i =1 j =1
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.
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.
Partial Applications
 Loan volume-based models (continued)
 Provide market benchmarks.
 Standard deviation measure of loan allocation
deviation.
N
sj =
2
(
X
X
)
 i, j
i
i =1
N
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 + i [total loan losses]
[ total loans ]
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.
Market Risks
Budi Purwanto
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
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.
Market Risk Measurement
 Important in terms of:





Management information
Setting limits
Resource allocation (risk/return tradeoff)
Performance evaluation
Regulation
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
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)
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.
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%.
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
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
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.
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
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.
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.
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.
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.
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.
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.
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
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.
Operational Risks
Budi Purwanto
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.
Sources of Operational
Risk





Technology
Employees
Customer relationships
Capital assets
External
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
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
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.
Impact on Wholesale Banking
 Improvements to cash management





Controlled disbursement accounts
Account reconciliation
Wholesale lockbox
Electronic lockbox
Funds concentration
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
Impact on Wholesale Banking
(continued)
 Verifying identities
 Issue of law enforcement access to
encrypted data since September 11, 2001
 Assisting small business entry into ecommerce
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
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
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
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.
Effects on Costs
(continued)
 Economies of scale
 Optimal size depends on shape of average
cost curve.
AC
AC
Size
AC
Size
Size
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
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)
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.
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.
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.
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
Other Operational Risks
 Employees







Turnover
Key personnel
Fraud
Errors
Rogue trading (Barings, Allied Irish/Allfirst)
Money laundering
Confidentiality breach
Technology Risks







Programming error
Model risk
Mark-to-market error
Management information
IT/Telecomm systems outage
Technology provider failure
Contingency planning
Customer Relationship
Risks
 Contractual disagreement
 Dissatisfaction from poorly performing
technology
 Default
Capital Asset Risk




Safety
Security
Operating costs
Fire/flood
External risks







External fraud
Taxation risk
Legal risk
War
Market collapse
Reputation risk
Relationship risk
Controlling Operational
Risk
 Loss prevention:
 Training, development, review of employees
 Loss control:
 Planning, organization, back-up
 Loss financing:
 External insurance
 Loss insulation:
 FI capital
Optimal Risk Management
Cost
RME
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
Liquidity Risk
Budi Purwanto
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.
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.
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
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
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
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
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.
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*)
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:
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
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.
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.
Alleviating Bank Runs:
 Regulatory measures to reduce likelihood
of bank runs:
 FDIC
 Discount window
 Not without economic costs.
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.
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.
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.
Liquid Asset Management
 Examples: T-bills, T-notes, T-bonds
 Benefits of holding large quantities of liquid
assets
 Costs of holding liquid assets
Liquid Asset Management
 Reasons for regulating minimum holdings
of liquid assets:
 Monetary policy
 Taxation
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.
Return-Risk Trade-off
 Cash immediacy versus reduced return
 Constrained optimization
 Privately optimal reserve holdings
 Regulator imposed reserve holdings
U.S. Cash Reserve
Requirements
 Incremental reserve requirements for
transaction accounts:
 First $5.5 million
 $5.5 million to $42.8 million
 $42.8 million +
0.0%
3.0%
10.0%
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
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
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
Undershooting
 DI has two options near the end of the
maintenance period
 Liquidate assets
 Borrow reserves
 fed funds
 repurchase agreements
Discount Window
 Reserve shortfalls in the past
 Discount window borrowing
 discount rate usually lower than market rates
 Risks of gaming the system
Overshooting
 First 4 percent can be carried forward to
next period
 Excess reserves typically low due to
opportunity costs
 Knife-Edge problem
Funding Risk versus Cost
Funding Cost
Funding Risk
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.
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.
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.
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
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
Other Borrowings




Bankers acceptances
Commercial paper
Medium-term notes
Discount window loans
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.
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.
Liquidity Risk in Other FIs
 Insurance companies
 Diversify across contracts
 Hold marketable assets
 Securities firms
 Example: Drexel Burnham Lambert
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.