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Lecture 6
Value at Risk Models: applications, limits &
expected shortfall
Giampaolo Gabbi
Financial Investments and Risk Management
MSc in Finance
2017 - 2018
2
Agenda
Main applications of VaR models
Problems of VaR models
Size of losses
Subadditivity
An alternative risk measure: expected shortfall or conditional value at risk (CVaR)
3
VaR models applications
1 Creating a common risk language
2 Estimating risk-adjusted performance (RAP)
3 Setting risk limits
4 Risk-Adjusted Pricing
4
VaR models applicationsA common language
VaR
Maximum potential loss ... 1. ... with a certain confidence level2. ... within a certain time horizon
Common definition of risk
This allows to:
• aggregate different types of risks;
• compare the amount of risk taken by different BUs;
• get a measure of global risk for the whole bank.
5
Common language
150.4%25,033,22
74,69%25,036,733,2000.100
2
BTPVaR
Price 100 EUR/USD Spot 1
Notional Value EUR 100,000 Notional USD 100,000
Maturity 10 years Market Value EUR 5.407
Coupon 6% Maturity 1 year
Modified Duration 7,36 Strike 1
Modified Convexity 69,74 Implied Volatility 10%
Yield to Maturity 6% Delta 0,5
Treasury Bond USD Call Option
Risk Profile of 2 alternative positions
6
Risk-adjusted Performance (RAP)
• Risk adjusted profitability ex-ante or ex-post
VaR
LPERAROC anteex
)&(
VaR
LPRAROC postex
&
7
VaR models applicationsRisk-adjusted Return on Capital (RAROC)
Bond Portfolio Equity Portfolio
Market Value € 25 mln € 25 mln
Monthly Profit € 150000 € 250000
Profitability 0.6% 1%
Volatility (IR/Stok Mkt) 0.5% 5,00%
Sensitivity (MD/Beta) 5 1
VaR 25*5*0.5%*2 = 1.25 m € 25*1*5%*2 = € 2.5 m
RAROC 150,000/1.25m = 12% 250,000/2.5 m = 10%
8
VaR models applicationsRisk-control (limits)
Example of risk limits
Treasury Bonds 320.000
Treasury Bills 140.000
FRA 240.000
IRS 300.000
Total 1.000.000
• Head of trading can modify risk-capital allocation (limits)• Each desk has to respect a maximum VaR constraint• If volatility increase, the single trader has to reduce her position
(market value) in order to avoid overcoming the limit
• Assume the head of Fixed-Income Trading has a global risklimit to assign to her traders of € 1 m
9
Some VaR models common critiques
VaR models do not consider exceptional events
VaR models do not consider customer relationships (short termism)
VaR models are based on unrealistic assumptions
VaR models produce different results
VaR models amplify markets instability
VaR models do not react quickly enough
10
VaR models real problems
They do not consider the size of losses
VaR with confidence level 99%
the probability of loosing more than VaR is only 1%…
…however, how much could we loose if these 1% probability events occur?
11
Example of Losses Relating to Two Stock Portfolios Losses (ranked starting from the worst one)
Portfolio H Portfolio K
1 150,000 60,000
2 120,000 56,000
3 100,000 55,000
4 70,000 53,000
5 60,000 51,000
6 50,000 50,000
7 48,000 45,000
8 45,000 40,000
9 42,000 35,000
10 40,000 30,000
VaR(99%) VaR99% 50,000 50,000
Maximum Loss Lmax 150,000 60,000
Maximum Excess Loss Lmax - VaR99% 100,000 10,000
Maximum Excess Loss/VaR 200% 20%
Expected Excess Loss E (L-VaR99% | L > VaR99%)
50,000 5,000
Expected Excess Loss/VaR 100% 10%
VaR models real problems
• 10 worst losses over 500 observations for two stock portfolios
Portfolio H is clearly riskier
The two portfolios have the same VaR
12
VaR models real problems
Subadditivity of risk measures
Example: portfolio Z = X+Y
If we abandon the normal distribution assumption of market factors returns VaR measures could, in some particular circumstances, not respect this property
)()()()( YFXFYXFZF
)()()( YVaRXVaRYXVaR
13
An alternative risk measure: expected shortfall
Expected Shortfall (ES) or Extreme Value at Risk (EVR): “the expected value of losses that the portfolio could suffer in the (1-c) worst
case during the time horizon T”
cLPEVaR )(
cc LLLEPEES /)(
14
An alternative risk measure: expected shortfall
• Example: portfolio of 2 bonds both with PD = 5% e recovery rate alternatively equal to 70% (prob. 1%) or 90% (prob. 4%)
Probability Bond A Bond B
1,00% 70 70
4,00% 90 90
95,00% 100 100
E(VM) 99,3 99,3
VaR(99%) 9,3 9,3
ES(99%) 29,30 29,30
Probability distribution of 2 bonds values
15
An alternative risk measure: expected shortfall
Event Probability A B A+B Cumulative Prob.
1 0,01% 70 70 140 0,01%
2 0,04% 70 90 160 0,05%
5 0,04% 90 70 160 0,09%
3 0,95% 70 100 170 1,04%
7 0,95% 100 70 170 1,99%
4 0,16% 90 90 180 2,15%
6 3,80% 90 100 190 5,95%
8 3,80% 100 90 190 9,75%
9 90,25% 100 100 200 100,00%
100,00%
Probability distribution of the bond portfolio
16
An alternative risk measure: expected shortfall
Event Probability A+B Cumulative Probability
1 0,01% 140 0,01%
2 0,08% 160 0,09%
3 1,90% 170 1,99%
4 0,16% 180 2,15%
5 7,60% 190 9,75%
6 90,25% 200 100,00%
100,00%
E(VM(A+B)) 198,60
VaR(99%) 28,60
ES(99%) 40,82
Probability distribution and risk measures of the portfolio