Market Risk by K61gtQv

VIEWS: 7 PAGES: 51

• pg 1
```									                          Market Risk
1.    Definition of Market Risk

2.    Interest Rate Risk: Cash Flow Risk

3.    Motivations to quantify Market Risk

4.    Calculating Market VAR

5.    VAR and Confidence Level

6.    VAR and Diversification Effect

7.    Multiperiod VAR

8.    Historical VAR

9.    Backtesting the VAR

10.   Portfolio Toolkit

11.   Portfolio VAR

12.   Pros and Cons of VAR Measures

13.   Market Risk Regulation

14.   Stress Testing
Defining Market Risk

   Interest rate shifts
   Foreign exchange shifts         Reduction in the
value of A/L,
   Commodity price shifts          securities, etc.
portfolios, etc.
   Equity price shifts

   Market risk affects :
   Banking book
Interest Rate Risk

   Shifts in the term structure of interest rate have two impacts

   A cash flow effect

   Market value effect

   To manage cash flow risk, banks can calculte the GAP:

NII  GAP x i
Interest Rate Risk:
Cash flow risk
ASSETS          LIABILITIES
RSA               RSL
400 @ 8%          600 @ 6%

FRA               FRL
600 @ 10%         400 @ 8%

Interest income = 32 + 60 = 92
Interest expenses = 36 + 32 = 68
NII = 24
Interest Rate Risk:
Cash flow risk
ASSETS          LIABILITIES
RSA                RSL
400 @ 10%          600 @ 8%

FRA                FRL
600 @ 10%          400 @ 8%

Interest income = 40 + 60 =100
Interest expenses = 48 + 32 = 80
NII = 20
Interest Rate Risk:
Cash flow risk
   Interest rates increase by 2% such that NII decreases by \$4
million.

   Using the GAP:

NII  \$200 x 0.02
NII  \$4

   For all RSA: duration and convexity are used to capture the
impact of interest rate.
Motivations to
Quantify Market Risk

   Required by many internal instances such as the Chief Risk Officer,
various risk management committees, etc, to track at any time the
exposure of the bank (or other FI’s) with respects to market risk.

   Limit setting.

   Risk components of performance evaluation.

   Economic capital allocation across the different business units.

   Required for regulatory purposes (Since Basel 98).
Calculating Market VAR
   Recognized as the best measure to capture market risk.

   VAR measure?
   How much could the FI loose on a given position, with
an x% probability of occurrence over a given time
frame?

   Example :
   A one-day VAR of \$20 million at a probability of 1%,
(or equivalently, at a confidence level of 99%) means
that one day out of 100, the bank could loose \$20
million or more

   Statistical Methodology
Calculating Market Var

Graphically :       Two possible Value-at-Risk measures

VAR-measure
Probability             no 1
distribution

1%

0   Expected         Return profits
VAR-measure
return profits
no 2
Calculating Market VAR
(Normal density function)

   Example :

A European bank currently has a \$120 million in foreign exchange
position. What is the one-day VAR at a confidence level of 95% (5%
probability of occurrence) knowing that the current \$US/€ is 1.20 ?

Based on an historical sample, assume that the mean and standard
deviation of the daily Δ% \$US/€ exchange rate are (continuously
compounding):

r  0.10%      and      ( r )  0.55%
Assume further that the exchange rate return (Δ% \$US/€)
follows a normal probability distribution function
Calculating Market VAR
(Normal density function)
•   VAR measure no 1: In this case, the deviation is
calculated from the mean. Therefore:

We look for Probr  VAR    %  1  c%. Thus,
Probz  z c    %
Probz  -1.65  5%
 r - r         
  (r)   -1.65  5%
Prob       
               
Probr  -1.65  (r)  r   5%
Probr  -1.65 0.0055  0.0010  5%
Calculating Market VAR
(Normal density function)
   Example (cont’d):

Prob r  0.008075   5%
where VAR  0.8075 %
The VAR can also be expressed in Euro:

1
VAR in Euro  -0.008075 \$US 120 million 
1.20 \$US/E
VAR in Euro   Euro 807 500
Calculating Market VAR
(Normal density function)
•   VAR measure no 2: In this case, the VAR is
calculated only using the standard deviation

We look for Prob r  VAR    %  1  c%. Thus,
Prob z  z c    %
Prob z  -1.65   5%
Prob r  -1.65   (r)   5%
Prob r  -1.65  0.0055   5%
Calculating Market VAR
(Normal density function)
   Example (cont’d):

Prob r  0.009075   5%
where VAR  0.9075 %
The VAR can also be expressed in Euro:

1
VAR in Euro  -0.009075 \$US 120 million 
1.20 \$US/E
VAR in Euro   Euro 907 500
Calculating Market VAR
(Normal density function)

   If the probability of occurrence is set at 1%, under the
VAR measure no 2, we obtain :

 (2.33  0.0055 )  Euro 100 million
  Euro 1 281 500

   The Var measure no 2 closely depends on the
standard deviation measure:

Var    N ( x)   -1
VAR and Confidence Level

Probability of occurence
High                                                Low

Less conservative:                               More conservative:
Low capital charges                              High capital charges
Increase in VAR value

Often, the level of confidence at which the Var is calculated is
closely related to the credit rating. For instance, a AA bank
with an S&P one-year probability of default of 0.03% might
choose a corresponding level of confidence of 99.97%.
VAR and Diversification Effect
   Example :

A European bank has a position in a USD bond with 6 year
to maturity, annual coupon of 8% and a yield to maturity
of 12%. The price of the bond is \$835.54 while its
duration is 4.88 years. The bank holds a \$120 million
(market value) position in that bond.
   Given a spot exchange rate of \$1.20/€ 1:

\$120 million
 Euro 100 million
1.20

   There are two sources of risk: the interest rate and the
currency risk.

   However, we can calculate individual VAR’s, the first captures
the interest risk while the second captures the currency risk
VAR and Diversification Effect
•   Interest rate VAR:

ΔP              Δy
 D 
P            (1  y)
Δy
rBond, y  D 
(1  y)
σ(Δy)
σ(rBond, y )  D 
(1  0.12)

•   Assuming  (y)  0.001

4.88  0.001
σ(rBond, y )                0.43%
1.12
VAR and Diversification Effect

•   Interest rate VAR at 1% (normal density function,
measure no. 2):

(2.33  0.0043  \$ 120 million )
VAR                                      Euro 1001900
1.20 \$ /Euro

•   Currency VAR at 1% (normal density function,
measure no. 2, see previous example):

- Euro 1 281 500
VAR and Diversification Effect

Total VAR         = Interest rate VAR + currency VAR
= € 1001900 + € 1 281 500
= € 2 283 400
   But that approach does not account for the diversification effect:
The return on the total position is :

rbond  rbond,y  rbond,ex

%∆ in the bond price            %∆ in the bond price
resulting from shifts in        resulting from shifts in \$/€
US interest rate                exchange rate
VAR and Diversification Effect
Thus:

σ 2 (rbond )  σ 2 (rbond,y  rbond,ex )
σ 2 (rbond )  σ 2 (rbond,y )  σ 2 (rbond,ex )  2  rbond,y ,rbond,ex σ(rbond,y )σ(rbond,ex )

σ(rbond )  σ 2 (rbond )  σ 2 (rbond,y )  σ 2 (rbond,ex )  2  rbond,y ,rbond,ex σ(rbond,y )σ(rbond,ex )
VAR and Diversification Effect

Assuming    r  bond,y rbond,ex
 0.5

 rp               0.43%2  0.55%2  2  0.5  0.43%  0.55%
          0.00007239

 0.8508%
Then, the total VAR is (normal density function):
VAR in Euro  2.33  0.008508  Euro 100 million
VAR in Euro   Euro 1 982 418


VAR and Diversification Effect

   The gain from diversification are:

€ 2 283 400      -    € 1 982 418 = € 300 982

No diversification   Diversification effect
effect

Correlation effect

   When there are more than one risk factor involves, the correlation
parameter,  , is responsable for a risk diversification effect. To see
that, rework the previous example with   0,   1 and   1 and look
at what happens to the Var calculation.
Multiperiod VAR

   Multiple-day VAR based on the one-day VAR :

n-day VAR = n½ x one-day VAR
Historical VAR
   The previous approach is based on the assumption of a
normal probability distribution function

   May be vaild for diversified portfolios with independant risk
factors
   Unlikely to be valid in many situations (data frequency, type of
instruments, etc.)

   Historial VAR :
1) Identification of an historical sample for the risk factors, at a
given frequency.

2) Application of those changes to the current risk factors.
Computation of the corresponding position.

3) Sorting of the payoff vector and identification of the appropriate
percentile on the empirical distribution.
Historical VAR
   An example :
A European bank has a position in a USD bond with
6 years to maturity, annual coupon of 8% while the
yield to maturity is 12%. The price of the bond is \$
835.54 while its duration is 4.88 years. The bank
holds a \$120 million position in that bond.
   The exposure is :
\$120 million
 Euro 100 million
1.20
   Current data :
interest rate   exchange rate duration
12%                1.20       4.88
Historical VAR
The historical changes in the risk factors :

n = 250        ∆% \$/Euro   ∆ 10-year rate
January 2, 2006       - 0.521%        0.02%
January 3, 2006     - 0.4405%         0.02%
January 4, 2006     - 0.0256%        -0.04%
▪                 ▪           ▪
▪                 ▪           ▪
▪                 ▪           ▪
December 23, 2006     - 0.563%        0.03%
December 24, 2006     - 1.004%       -0.05%
Historical VAR
   The return on this position :
rbond  rbond, y  rbond,ex

y
  D             %\$ /E
1  y 
 4.88
                y  %\$ /E
1  0.12 
   On January 2, 2006 :

rbond   4.357  0.0002  0.00521
rbond  0.6081%
   Euro variation in the bond position:

 Euro 100 million   0.00682
  Euro 608 074 .108
Historical VAR
   The procedure is repeated for every day of the historical sample :
Day           Euro gains/losses on the bond
January 2, 2006              - 608 074.108
January 3, 2006              - 507 812.391
January 4, 2006                149 032.398
▪                           ▪
▪                           ▪
▪                           ▪
December 23, 2006            - 694 284.502
December 24, 2006           - 786 181. 722

   The vector of gains/losses is then sorted from the highest to the
lowest : Rank Scenario       Euro gains/losses on the bond
1         96                1 691 388.41
2         231               1 625 185.38
3         127               1 535 152.77
▪          ▪                      ▪
▪          ▪                      ▪
238        191              - 1 360 529.24
▪          ▪                      ▪
240        120              - 1 737 629.56
Historical VAR

   The 1% VAR is :
Rank          Scenario   Euro gains/losses on the bond
238            191              - 1 360 529.24

   The 5% VAR is :

Rank          Scenario   Euro gains/losses on the bond
228             12              - 989 650.223
Historical VAR:
Confidence Interval
   The historical Var measure corresponds to a quantile of
the empirical distribution. Kandal and Stuart (1972, c.f.
Hull 2007 p. 220) derive an estimate of the
corresponding standard error:
1    q(1  q)
std(HVar) 
f ( x)    n
where q : quantile of the distribution
n : number of observations
f ( x) : postulated density function of the HVar

   Assumed f (x) is the normal probability density function.
Then, the confidence interval at a 95% confidence level
is:
 
                              
                    
prob HVar - std(Hvar) 1.96  HVar  HVar  std(Hvar) 1.96  95%

                                                       

Historical VAR:
Confidence Interval
   Based on the previous example:
1          0.05  0.95
std(HVar)                                   81278 .69
1.773087 e  07      240

   The value of f ( x)  1.756E  07 comes from Excel
assuming that the mean payoff is 0 and its std is
\$595860.22 and using the following command:

NORMINV(0.05,0,595860.22)=-980102.84
NORMDIST(-980102.84,0, 595860.22,False)=1.736E-07

Then, the confidence for the HVar is:
\$989650.22- 81278.49 1.96  HVar 
prob                                     95%
\$989650.22 81278.49 1.96         
Historical VAR:
Confidence Interval
   Remark: It is also possible to produce a confidence
interval via bootstrapping:
   Since n  250 , it is possible to sample 250 000 scenarios
of 1000  250 samples
   For each sample, calculate the HVar. With all the HVar
estimates, rank them from the Highest to the lowest
   This provides an empirical distribution of Var based on
1000 HVar estimates. Using quantiles, we know that:

 
                          
      
prob HVar (at 97.5%)  HVar  HVar (at 2.5%)  95%

                                        

Historical VAR
   Method completely nonparametric, no distributional
assumption
   Easy to apply
   Captures complex volatility and correlation effects

   Disavantages :
   Method is very sensitive to the choice of historical
data
   Historical sample may not account for structural
events
   In some cases, the data availability is a problem
Backtesting the Var
   Example:
 N=600 observations

 99%-day VAR. Exception occurs at 1% of the days

 0.01 x 600 = 6 is the expected number of exceedances

 Actual number of exceedances is 8. Is this statistically significant?

 Define x, a bernoulli variable, the number of exceedances. Given the
large number of observations, we can use the central limit theorem such
that

x  pN
z                  N(0,1)
p(1  p)N
   Hypothesis testing:

H o : x  pN  6
H1 : x  8
86          2
z                         0.82
0.01(1 0.01)600 2.43
At a 5% level of confidence, the critical value is 1.95. Therefore, we cannot not
reject H0.
Portfolio Toolkit
   Consider a portfolio composed of one stock (for a value of \$6000) and
one bond for (for a value of \$4000). The value of the portfolio is:
\$10000  \$6000  \$4000
p  sb
p  s   b
p    s  b
      
p     p      p
s s     b b
rp         
p s      p b
rp  ws rs  wb rb

   This last equation says that the return on the portfolio, r p , is a
weighted average of the return on the stock, rs , and the bond,
2                                                         rb.
Notice that  wi  1, with:
i 1
\$6000                       \$4000
ws            60%   and   wb            40%
\$10000                      \$10000
Portfolio Toolkit
The   return on a portfolio is given by:
m                    m
rp   x i ri   with      x       i   1
i 1                  i 1

where ri , is the return on asset i, xi , the proportion of asset i
in the portfolio and m , the number of assets in the portfolios.

   The corresponding variance of the portfolio is (say m  1000)

1000               1000 1000
V(rp )   x i (ri )    x i x j  ij  (ri ) (r j )
2

i 1              i 1 j 1

i j
Portfolio VAR

   Under the normality assumption:
Var %   p N -1 (rp )

but the term  (r )  V(r ) requires the
p         p

calculation of (1000 x 999)/2 terms  ij.
The problem is impractical!
Portfolio VAR

s          2s
s     F  0.5 2 F 2
F         F

where s and F are respectively the value of
an asset and a market factor.

   Consider a portfolio of 500 stocks and 500
bonds:
500          500
rp   w si rsi   w bi rbi
i 1         i 1
Portfolio VAR
   From portfolio theory, we know that:
rsi   0  1 rm   si

Such that the second derivative is equal to 0. In
this case, F=market index.

   For bonds, based on duration and convexity, we
have:
b i   2
 bi
b i          y  0.5      y 2
y            y 2
b i   b i y           2bi 1
         y  0.5          y 2
bi    y b i           y 2 b i
rbi  D m oiy  0.5C xr y 2
in this case, F=y.
Portfolio VAR
   The 500 beta measures can be aggregated to
generate the portfolio beta.

   The 500 duration and convexity measures can be
aggregated to generate the portfolio duration
and convexity. Therefore after a few
manipulations:

rp   p rm  Dmopy  0.5C xrp y   2
Portfolio VAR
   Under an historical simulation, the previous
equation is applied directly.

   Under the assumption that the returns on the
portfolio are drawn from a normal distribution,
we can use the variance equation of the
portfolio:
0.5
  p  (rm )  D m op  (y )  0.25C xrp  (y ) 
2       2        2       2            2      2 2

                                                  
          2  p D m opcov(rp,rm)                 
 (rp )                                                    
          2  p C xrp cov(rp,y 2 )              
                                                  
          2D m opC xrp cov(y, y 2 )            
Portfolio Var

   Exemple: In class
Pros and Cons of VAR measures
   Pros :
 Provide a risk measure that is easly aggregated

 Captures netting effects and risk diversification

 Consistent measure across risk factors, securities, etc.

 Direct translation into risk limits

 Facilitate the communication amongst managers

   Cons :
 Require a choice of methodology. Different approaches
may lead to different VAR estimates
 VAR measures are not made to capture extreme, unlikely
events. Other approaches are required
 Estimates only valid over short horizion
Market Risk-VAR
Deutsche Bank
Market Risk Regulation

   Basel 1998 :
   Banks must have capital charges to cover exposures
with respect to market risk within the trading book,
and with respect to the credit risk involved with the

   The regulation affects both on- and off- balance
sheets.

   Large banks are allowed to use their own-internal
model based on the VAR methodology. Other banks
must use the standard model proposed by BIS.
Market Risk Regulation

   Capital charges :

Credit risk of individual issuers

K  (10  day VAR, 1%)  4  (10  day specific risk VAR)

May be changed by the regulator. K = 3 (minimum value)

   Total capital = 0.08 x (Credit risk RWA + Market risk RWA)

   where market risk RWA = 12.5 x capital charges
above
Market Risk Regulation: Basel 98

Capital charges for specific risks (%)
Example :                                Internal   Standardized
Portfolio of 100                          model       approach
1 € bonds
diversified           AAA                  0.26         1.6
across                AA                   0.77         1.6
industries            A                    1.00         1.6
BBB                  2.40         1.6
BB                   5.24          8
B                    8.45          8
CCC                 10.26          8

Source: Crouhy, Galai and Mark (2001)
Market Risk Regulation
   The Group of 30 policy’s recommendations
(1993) :

   Series of recommendations on price risk management for
dealers, end-users of derivatives, legislators and regulators.

   Recommendation   2 : Mark-to-market process
   Recommendation   5 : Measuring market risk using a VAR
metho.
   Recommendation   6 : Importance of stress testing
   Recommendation   8 : Banks should have an independant risk
management function
Stress Testing
VAR                      Market                 Stress
Risk                  testing

Standard environments                               Extreme events

   Stress testing: assessments of potential losses under specific
scenarios

   Scenarios from the Derivative Policy Group (1995) :

   Shifts in the yield curve ± 100 bps
   Twist risk of ± 25 bps
   Etc.
   Basel requires stress-testing in addition to Var calculation
Stress Testing
     Example: Stress testing at BMO:

Stress-testing with respect to interest rate at BMO
Year 2003                                  Year 2002
Economic Value           Earnings sensitivity    Economic Value      Earnings sensitivity
sensitivity               (12 months)            sensitivity          (12 months)
100 bps increase            - \$ 202.3 millions          \$ 10.8 millions      - \$ 152.7 millions      \$ 1.1 millions
100 bps decease              \$ 142.7 millions          - \$ 17.6 millions      \$ 123.8 millions      - \$ 0.1 millions
200 bps increase            - \$ 431.8 millions         \$ 431.8 millions      - \$ 354.1 millions     - \$ 3.9 millions
200 bps decease             - \$ 181.2 millions         - \$ 61.2 millions      - \$ 180 millions     - \$ 70.7 millions

Source: Bank of Montreal, annual report, 2003

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