B40.3312 Policymaking in
Financial Institutions
Professor A. Sinan Cebenoyan
NYU-Stern-Finance
Copyright 2001 A. S. Cebenoyan
1
Market Risk
• Market Risk (Value at Risk, VAR): dollar exposure amount
(uncertainty in earnings) resulting from changes in market
conditions such as the price of an asset, interest rates,
market volatility, and market liquidity.
• The five reasons for market risk management:
–
–
–
–
-
Management information (senior management sees exposure)
Setting Limits(limits per trader)
Resource Allocation (identify greatest potential returns per risk)
Performance Evaluation (return-risk per trader
Bonus)
Regulation (provide private sector benchmarks)
Copyright 2001 A. S. Cebenoyan
2
JPM’s RiskMetrics Model
• Large commercial banks, investment banks, insurance
companies, and mutual funds have all developed market
risk models (internal models). Three major approaches to
these internal models:
– JPM Riskmetrics
– Historic or back-simulation
– Monte Carlo simulation
• We focus on JPM Riskmetrics to measure the market risk
exposure on a daily basis for a major FI.
• How much the FI can potentially lose should market
conditions move adversely:
Market Risk = Estimated potential loss under adverse
circumstances
Copyright 2001 A. S. Cebenoyan
3
Daily earnings at risk= ($ market value of position) x (Price volatility)
where,
Price volatility = (Price Sensitivity) x (Adverse daily yield move)
We next look at how JPM Riskmetrics model calculates DEaR in
three trading areas: Fixed income, Foreign exchange, and Equities,
and how the aggregate risk is estimated.
Market Risk of Fixed Income Securities
Suppose FI has a $1 million market value position in 7-yr zero coupons with a face
value of $1,631,483.00 and current annual yield is 7.243 .
Daily Price volatility =
dP
D

( R )   MD ( R )
P
1 R
7
The modified duration = MD  D 
 6.527
1 R
(1.07243)
for this bond
Copyright 2001 A. S. Cebenoyan
4
If we make the (strong and unrealistic) assumption of normality in
yield changes, and we wish to focus on bad outcomes, i.e., not just
any change in yields, BUT an increase in yields that will only be
possible with a probability, i.e., a yield increase that has a chance
of 5%, or 10%, or 1%…(We decide how likely an increase we wish
to be worried about). Suppose we pick 5 %, i.e., there is 1 in 20
chance that the next day’s yield change will exceed this adverse move.
If we can fit a normal distribution to recent yield changes and get a mean of 0 and
standard deviation of 10 basis points (0.001), and we remember that 90% of the area
under the normal distribution is found within +/- 1.65 standard deviations, then
we are looking at 1.65s as 16.5 basis points. Our adverse yield move.
Price Volatility = -MD (R) = (-6.527) (.00165) = -.01077
DEaR = DEAR = ($ market value of position) (Price Volatility)
= ($1,000,000) (.01077)
dropping the minus sign
= $10,770
The potential daily loss with 5% chance
For multiple N days, DEAR should be treated like s, and VAR computed as:
VAR  DEAR  N
Copyright 2001 A. S. Cebenoyan
5
Foreign Exchange
Suppose FI has SWF 1.6 million trading position in spot Swiss francs. What is the
DEAR from this?
•First calculate the $ amount of the position
•$ amount of position = (FX position) x ($/SWF)
= (SWF 1.6million) x (.625) = $ 1 million
If the standard deviation (s) in the recent past was 56.5 basis points, AND
we are interested in adverse moves that will not be exceeded more than 5%
of the time, or 1.65s:
FX volatility = 1.65(56.5) 93.2 basis points
THUS,
DEAR = ($ amount of position) x (FX volatility)
= ( $1million) x (.00932)
= $9,320
Copyright 2001 A. S. Cebenoyan
6
Equities
Remember your CAPM:
Total Risk = Systematic risk + Unsystematic risk
s
2
it

 s
2
2
it
mt

s
2
eit
If the FI’s trading portfolio is well diversified, then its beta will be close to 1, and
the unsystematic risk will be diversified away….leaving behind the market risk.
Suppose the FI holds $1million in stocks that reflect a US market index, Then
DEAR
= ($ value of position) x (Stock market return volatility)
= ($1,000,000) (1.65 s m)
If the standard deviation of daily stock returns on the market in the recent past
was 2 percent, then 1.65(s m)= 3.3 percent
DEAR = ($1,000,000) (0.033) = $33,000
Copyright 2001 A. S. Cebenoyan
7
Portfolio Aggregation
We need to figure out the aggregate DEAR, summing up won’t do,
REMEMBER:
s
2
x y
s
s
2
x y
 [ E{( x  y )  E ( x  y )}]2
 [ E{( x  E ( x))  ( y  E ( y ))}]2
2
 E ( x  E ( x)) 2  E ( y  E ( y )) 2
x y
 2{E ( x  E ( x)) E ( y  E ( y ))}
s
2
x y

s

x
2
s
2
y
 2  xys xs y
If the correlations between the 3 assets are:
Bond
Bond
SWF/$
-.2
SWF/$
US Stock Index
.4
.1
Copyright 2001 A. S. Cebenoyan
8
Then the risk of the whole portfolio, DEAR treated like s, will be




DEAR portfolio  





DEAR
DEAR
DEAR
DEAR
DEAR
2
b
2
2

us
DEAR 
 2
DEAR
 2
DEAR
 2
DEAR
swf
us



b 

b

us 



swf
b.swf
b.us
us.swf
swf
1/ 2
Substituting the values we have:
(10 .77 ) 2  (9.32 ) 2  (33) 2 


DEAR portfolio    2( .2)(10 .77 )( 9.32 )

 2(. 4)(10 .77 )(33)
  2(. 1)(9.32 )( 33)



Copyright 2001 A. S. Cebenoyan
1/ 2
= $39,969
9
•BIS Standardized Framework for Market Risk
•Applicable to smaller banks.
•Fixed Income
•Specific Risk charge (for liquidity or credit risk quality)
•General Market Risk charge
•Vertical and horizontal offsets
•Foreign Exchange
•Shorthand method: (8% of the maximum of the aggregate net
long or net short positions)
•Longhand method: Net position, Simulation, worst case scenario
amount is charged 2%
Copyright 2001 A. S. Cebenoyan
10
•Equities
•Unsystematic risk charge (x-factor): 4% against the gross position
•Systematic risk charge (y-factor): 8% against the net position
•Large Bank Internal models
•BIS standardized framework was criticized for crude risk measurements
+ lack of correlations + incompatability with internal systems.
•BIS in 1995 allowed internal model usage by large banks with
conditions:
•Adverse change is defined as 99th percentile - Minimum holding
period is 10 days - correlations allowed broadly
•Proposed capital charge will be the higher of the previous day’s
VAR, or the average daily VAR over the last 60 days times a factor
(at least 3). Tier 2 and Copyright
3 allowed
up to 250% of Tier 1.
2001 A. S. Cebenoyan
11
Consolidation in Banking
•
•
•
•
•
•
Berger, Demsetz, Strahan article
Chapter 14 Saunders
Economies of Scale
Economies of Scope
Efficiencies
Consolidations
Copyright 2001 A. S. Cebenoyan
12
Copyright 2001 A. S. Cebenoyan
13
•Reduction in numbers 30%
•Concentration -- largest 8 banks’ share from 22 to
36%
•MSA Herfindahls declined
•Total bank offices up by 17%
•Bank + Thrift offices down by 0.1%, thrifts
acquired by banks
Copyright 2001 A. S. Cebenoyan
14
Copyright 2001 A. S. Cebenoyan
15
•Several hundred M&A’s each year
•Supermegamergers
•Citi-Travelers
•BankAmerica-Nations
•BancOne-First Chicago
•Norwest-WellsFargo
•UBS-Swiss Bank Corp >>largest in EU
Copyright 2001 A. S. Cebenoyan
16
Copyright 2001 A. S. Cebenoyan
17
•Not much change in securities brokerage, life and
P&A insurance
•Securities and Life ins. Less concentrated in 90’s
•P&A more concentrated
•Substantial reduction in Thrifts
•CU’s very unconcentrated because of nature
Copyright 2001 A. S. Cebenoyan
18
Consolidation across sectors rare in US, more important in EU
Copyright 2001 A. S. Cebenoyan
19
Similar to earlier panel, International M&A’s within sectors exceed
across sectors, but across M&A’s relatively more important in EU
Copyright 2001 A. S. Cebenoyan
20
US much more fragmented, but not ‘overbranched’
Copyright 2001 A. S. Cebenoyan
21
• Causes of Financial Consolidation
• value-maximization: increase market power
(set prices, increase concentration, market
power) increase efficiency (more efficient takes
over less efficient)
Too big to fail protections
• Non-value maximizing: The role of managers:
weak corporate control, empire building,
compensation and size, too-big-to fail,
entrenched managers, etc...
Copyright 2001 A. S. Cebenoyan
22
•Role of government: approve-disapprove,excessive
market power, too-big-to-fail, CRA requirements
may encourage acquisition of weaker institutions.
•Why is consolidation increasing?
•Technological progress
•Improvements in financial conditions (internal
capital markets)
•accumulation of excess capacity – financial
distress
Copyright 2001 A. S. Cebenoyan
23
•International consolidation, globalization
•Deregulation – Riegle-Neal Interstate Banking
and Branching Efficiency Act of 1994 – though
with limits
•
•
•
• Early results generally positive
Copyright 2001 A. S. Cebenoyan
24
Internet Banking: some early
evidence
Copyright 2001 A. S. Cebenoyan
25
Market share and concentration
Copyright 2001 A. S. Cebenoyan
26
Loan Distribution
Copyright 2001 A. S. Cebenoyan
27
Expenses and Profitability
Copyright 2001 A. S. Cebenoyan
28