value at risk ief 217a: lecture section 5 fall 2002 jorion chapter 5
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Value At Risk
IEF 217a: Lecture Section 5
Fall 2002
Jorion Chapter 5
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Outline
• Computing VaR
• Interpreting VaR
• Time Scaling
• Regulation and VaR– Jorion 3, 5.2.5-5.2.6
• Estimation errors
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VaR Roadmap
• Introduction
• Methods– Reading: Linsmeier and Pearson
• Easy example
• Harder example: – Linsmeir and Pearson
• Monte-carlo methods and even harder examples– Jorion
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Value at Risk (VaR)History
• Financial firms in the late 80’s used it for their trading portfolios
• J. P. Morgan RiskMetrics, 1994
• Currently becoming:– Wide spread risk summary– Regulatory
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Why VaR?
• Risk summary number– Relatively simple– Relatively standardized
• Give high level management risk in 1 number
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What is VaR?
• Would like to know maximum amount you stand to lose in portfolio
• However, the max might too large
• 5% VaR is the amount that you would lose such that 5% of outcomes will lose more
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5% VaR = 84: 100 Start ValueNormal Distribution (std = 10)
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Value at Risk: Methods
• Methods (Reading: Linsmeier and Pearson)– Historical– Delta Normal– Monte-carlo– Resampling
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Historical
• Use past data to build histograms
• Method:– Gather historical prices/returns– Use this data to predict possible moves in the
portfolio over desired horizon of interest
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Easy Example
• Portfolio:– $100 in the Dow Industrials– Perfect index tracking
• Problem– What is the 5% and 1% VaR for 1 day in the
future?
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DataDow Industrials
• dow.dat (data section on the web site)
• File:– Column 1: Matlab date (days past 0/0/0)– Column 2: Dow Level– Column 3: NYSE Trading Volume (1000’s of
shares)
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Matlab and Data FilesKaplan: Appendix C
• All data in matrix format
• “Mostly” numerical
• Two formats– Matlab format filename.mat– ASCII formats
• Space separated
• Excel (csv, common separated)
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Loading and Saving
• Load data– “load dow.dat”– Data is in matrix dow
• Save data– ASCII
• save -ascii filename dow
– Matlab• save filename dow
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Example: Load and plot dow data
• Matlab: pltdow.m
• Dates:– Matlab datestr function
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Back to our problem
• Find 1 day returns, and apply to our 100 portfolio
• Matlab: histdvar.m
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Value at Risk: Methods
• Methods (Reading: Linsmeier and Pearson)– Historical– Delta Normal– Monte-carlo– Resampling
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Delta Normal
• Make key assumptions to get analytics– Normality– Linearization
• Dow example:– Assume returns normal mean = m, std = s
• 5% return = -1.64*s + m
• 1% return = -2.32*s + m
– Use these returns to find VaR– matlab: dnormdvar.m
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Compare With Historical
• Fatter tails
• Plot Comparison: twodowh.m
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Longer Horizon: 10 Days
• Matlab: hist10d.m
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Value at Risk: Methods
• Methods (Reading: Linsmeier and Pearson)– Historical– Delta Normal– Monte-carlo– Resampling
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Monte-Carlo VaR
• Make assumptions about distributions
• Simulate random variables
• matlab: mcdow.m
• Results similar to delta normal
• Why?– More complicated portfolios and risk measures– Confidence intervals: mcdow2.m
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Value at Risk: Methods
• Methods (Reading: Linsmeier and Pearson)– Historical– Delta Normal– Monte-carlo– Resampling
![Page 23: Value At Risk IEF 217a: Lecture Section 5 Fall 2002 Jorion Chapter 5](https://reader036.vdocuments.net/reader036/viewer/2022062407/56649d535503460f94a3010f/html5/thumbnails/23.jpg)
Resampling (bootstrapping)
• Historical/Monte-carlo hybrid– Also known as bootstrapping
• We’ve done this already– data = [5 3 -6 9 0 4 6 ];– sample(n,data);
• Example– rsdow.m
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VaR Roadmap
• Introduction
• Methods– Reading: Linsmeier and Pearson
• Easy example
• Harder example: – Linsmeir and Pearson
• Monte-carlo methods and even harder examples– Jorion
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Harder Example
• Foreign currency forward contract
• 91 day forward
• 91 days in the future– Firm receives 10 million BP (British Pounds)– Delivers 15 million US $
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Mark to Market Value(values in millions)
)1($15
)1(10
)$
($rr
BPBP
valBP
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Risk Factors
• Exchange rate ($/BP)• r(BP): British interest rate• r($): US interest rate• Assume:
– ($/BP) = 1.5355– r(BP) = 6% per year– r($) = 5.5% per year– Effective interest rate = (days to maturity/360)r
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Find the 5%, 1 Day VaR
• Very easy solution– Assume the interest rates are constant
• Analyze VaR from changes in the exchange rate price on the portfolio
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Mark to Market Value(current value in millions $)
300,331$
)055.0)360/91(1(
$15
)06.0)360/91(1(
10)5355.1(
BPval
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Mark to Market Value(1 day future value)
)055.0)360/90(1(
$15
)06.0)360/90(1(
10)5355.1)(1(
BPxval
X = % daily change in exchange rate
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X = ?
• Historical
• Normal
• Montecarlo
• Resampled
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Historical
• Data: bpday.dat
• Columns– 1: Matlab date– 2: $/BP– 3: British interest rate (%/year)– 4: U.S. Interest rate (%/year)
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BP Forward: Historical
• Same as for Dow, but trickier valuation
• Matlab: histbpvar1.m
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BP Forward: Monte-Carlo
• Matlab: mcbpvar1.m
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BP Forward: Resampling
• Matlab: rsbpvar1.m
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Harder Problem
• 3 Risk factors– Exchange rate– British interest rate– U.S. interest rate
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3 Risk Factors1 day ahead value
)055.0)1)(360/90(1(
$15
)06.0)1)(360/90(1(
10)5355.1)(1(
z
y
BPxval
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Daily VaR AssessmentHistorical
• Historical VaR
• Get percentage changes for – $/BP: x– r(BP): y– r($): z
• Generate histograms
• matlab: histbpvar2.m
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Daily VaR AssessmentResample
• Historical VaR
• Get percentage changes for – $/BP: x– r(BP): y– r($): z
• Resample from these
• matlab: rsbpvar2.m
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Resampling Question:
• Assume independence?– Resampling technique differs– matlab: rsbpvar2.m
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Risk Factors and Multivariate Problems
• Value = f(x, y, z)
• Assume random process for x, y, and z
• Value(t+1) = f(x(t+1), y(t+1), z(t+1))
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New Challenges
• How do x, y, and z impact f()?
• How do x, y, and z move together?– Covariance?
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Delta Normal Issues
• Life is more difficult for the pure table based delta normal method
• It is now involves– Assume normal changes in x, y, z– Find linear approximations to f()
• This involves partial derivatives which are often labeled with the Greek letter “delta”
• This is where “delta normal” comes from
• We will not cover this
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Monte-carlo Method
• Don’t need approximations for f()
• Still need to know properties of x, y, z– Assume joint normal– Need covariance matrix
• ie var(x), var(y), var(z) and
• cov(x,y), cov(x,z), cov(y,z)
• Next section, and Jorion