Value at Risk
Chapter 20
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull 2010
1
The Question Being Asked in VaR
“What loss level is such that we are X% confident it will not be exceeded in N business days?”
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
2
VaR and Regulatory Capital
Regulators base the capital they require banks to keep on VaR
The market-risk capital is k times the 10-day 99% VaR where k is at least 3.0
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
3
VaR vs. Expected Shortfall
(See Figures 20.1 and 20.2, page 431)
VaR is the loss level that will not be exceeded with a specified probability
Expected shortfall is the expected loss given that the loss is greater than the VaR level
Although expected shortfall is theoretically more appealing than VaR, it is not widely used
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Advantages of VaR
It captures an important aspect of risk
in a single number
It is easy to understand
It asks the simple question: “How bad can things get?”
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Historical Simulation
Create a database of the daily movements in all market variables.
The first simulation trial assumes that the percentage changes in all market
variables are as on the first day
The second simulation trial assumes that the percentage changes in all market
variables are as on the second day
and so on
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Historical Simulation continued
Suppose we use 501 days of historical data
Let vi be the value of a market variable on day i
There are 500 simulation trials
The ith trial assumes that the value of the market variable tomorrow is
v500
vi
vi −1
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Historical Simulation continued
The portfolio’s value tomorrow is calculated for each simulation trial
The loss between today and tomorrow is then calculated for each trial (gains are negative losses)
The losses are ranked and the one-day 99% VaR is set equal to the 5 th worst loss
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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The Model-Building Approach
The main alternative to historical simulation is to make assumptions about the
probability distributions of return on the market variables
This is known as the model building approach or the variance-covariance approach
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Daily Volatilities
In option pricing we express volatility as volatility per year
In VaR calculations we express volatility as volatility per day
σ day =
σ year
252
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Daily Volatility continued
Strictly speaking we should define σday as the standard deviation of the continuously
compounded return in one day
In practice we assume that it is the standard deviation of the percentage change in one day
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
11
Microsoft Example
We have a position worth $10 million in Microsoft shares
The volatility of Microsoft is 2% per day (about 32% per year)
We use N = 10 and X = 99
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
12
Microsoft Example continued
The standard deviation of the change in the portfolio in 1 day is $200,000
The standard deviation of the change in 10 days is
200,000 10 = $632,456
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
13
Microsoft Example continued
We assume that the expected change in the value of the portfolio is zero (This is OK for short time
periods)
We assume that the change in the value of the portfolio is normally distributed
Since N(–2.33)=0.01, the VaR is
2.33 × 632,456 = $1,473,621
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
14
AT&T Example
Consider a position of $5 million in AT&T
The daily volatility of AT&T is 1% (approx 16% per year)
The S.D per 10 days is
The VaR is
50,000 10 = $158,144
158,114 × 2.33 = $368,405
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Portfolio (See Example 20.1)
Now consider a portfolio consisting of both Microsoft and AT&T
Suppose that the correlation between the returns is 0.3
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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S.D. of Portfolio
A standard result in statistics states that
σ X +Y = σ X + σY + 2ρσ X σ Y
2 and ρ =2 0.3. The standard deviation of the change in
In this case σX = 200,000 and σY = 50,000
the portfolio value in one day is therefore 220,227
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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VaR for Portfolio
The 10-day 99% VaR for the portfolio is
220,227 × 10 × 2.33 = $1,622,657
The benefits of diversification are
(1,473,621+368,405)–1,622,657=$219,369
What is the incremental effect of the AT&T holding on VaR?
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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The Linear Model
We assume
The daily change in the value of a portfolio is linearly related to the daily returns from market
variables
The returns from the market variables are normally distributed
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Markowitz Result for Variance of
Return on Portfolio
n
n
Variance of Portfolio Return = ∑∑ ρ ij wi w j σ i σ j
i =1 j =1
wi is weight of ith instrument in portfolio
2
σ i is variance of return on ith instrument
in portfolio
ρ ij is correlation between returns of ith
and jth instrument s
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
20
VaR Result for Variance of
Portfolio Value (α i = wiP)
n
∆P = ∑ α i ∆xi
i =1
n
n
σ 2P = ∑∑ ρ ij α i α j σ i σ j
i =1 j =1
n
2
i
i =1
σ 2P = ∑ α σ i2 + 2∑ ρ ij α i α j σ i σ j
i< j
σ i is the daily volatility of ith instrument (i.e., SD of daily return)
σ P is the SD of the change in the portfolio value per day
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Covariance Matrix (vari = covii)
(Table 20.6, page 441)
var1
cov 21
C = cov 31
cov
n1
cov12
cov13
var2
cov 23
cov 32
var3
cov n 2
cov n 3
cov1n
cov 2 n
cov 3n
varn
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Alternative Expressions for σ P2
page 441
n
n
σ 2P = ∑∑ cov ij α i α j
i =1 j =1
σ 2P = α T Cα
where α is the column vector whose ith
element is αi and α T is its transpose
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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Handling Interest Rates
We do not want to define every bond as a different market variable
We therefore choose as assets zero-coupon bonds with standard maturities: 1month, 3 months, 1 year, 2 years, 5 years, 7 years, 10 years, and 30 years
Cash flows from instruments in the portfolio are mapped to bonds with the standard
maturities
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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When Linear Model Can be Used
Portfolio of stocks
Portfolio of bonds
Forward contract on foreign currency
Interest-rate swap
Fundamentals of Futures and Options Markets, 7th Ed, Ch 20, Copyright © John C. Hull
2010
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