marin bozic - university of minnesota mfm seminar, minneapolis, september 28, 2012
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Practical Issues In Pricing (and Using) Asian Basket Options: A Case of Livestock Gross Margin Insurance. Marin Bozic - University of Minnesota MFM Seminar, Minneapolis, September 28, 2012. Room 1: A Barn on Fire. Nature of risk in the dairy sector. Real price risk? - PowerPoint PPT PresentationTRANSCRIPT
Practical Issues In Pricing (and Using) Asian Basket Options:
A Case of Livestock Gross Margin Insurance
Marin Bozic - University of Minnesota
MFM Seminar, Minneapolis, September 28, 2012
2
3
Room 1: A Barn on Fire
Nature of risk in the dairy sector
Real price risk? Prolonged Period of Margins Much Below Average
20022003
20042005
20062007
20082009
20102011
0.002.004.006.008.00
10.0012.0014.0016.00
Dairy Margin, Foundation for the Future, NMPF
Livestock Gross Margin Insurance for Dairy Cattle (LGM-Dairy)
Jan Feb
Mar
Apr May
Jun Jul Aug
Sep
Oct Nov
Dec
Purchase at End of
Month
No Coverage
1 2 3 4 5 6 7 8 9 10
Insurance Contract Period
Farmer must decide:• Monthly target milk marketings (Mt+i) • expected feed usage (Ct+i, SBMt+i)• Gross Margin Deductible (D)
11 11 11
2 2 2
Margin Guarantee = M C SBMt i t i t i t i t i t i
i i if D M f C f SBM
How is LGM-Dairy priced?
Jan Feb
Mar
Apr May
Jun Jul Aug
Sep
Oct Nov
Dec
Purchase at End of
Month
No Coverage
1 2 3 4 5 6 7 8 9 10
Insurance Contract Period
• Extract information regarding expected prices and volatilities from futures prices and at-the-money options
• Calculate correlations based on historical data• Use Monte Carlo methods to simulate indemnities• Price of the Asian Basket Option set at mark-up over
actuarially fair price (e.g. expected indemnity).
• Identify expected milk marketings, feed amounts
• Choose target IOFC margin to protect• Insure equal percentage of each month’s
production, e.g. flat coverage for 10 months.
A Naïve approach to LGM-Dairy
• Identify expected milk marketings, feed amounts
• Choose target IOFC margin to protect• Find a least-cost profile that protects the
target IOFC.
A (bit less) Naïve approach to LGM-Dairy
A (bit less) Naïve approach to LGM-Dairy
1 2 3 4 5 6 7 8 9 100%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Insurable Month
Cove
rage
Per
cent
age
Home-feed profile:Insuring 1st-10th month
050
100150200250300350400
024681012141618
LGM Premium PaidActual MarginMargin with LGM Net Indemnity
$/Mg of Milk $/cwt of Milk
Home-feed profile:Insuring 1st-3rd month.
050
100150200250300350400
024681012141618
LGM Premium Paid Actual Margin
Margin with LGM Net Indemnity
$/Mg of Milk $/cwt of Milk
Class III Milk Futures: Open Interest
Sep-12
Oct-12
Nov-12
Dec-12
Jan-1
3
Feb-13
Mar-13
Apr-13
May-13
Jun-1
3Ju
l-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-1
40
1,000
2,000
3,000
4,000
5,000
6,000
Home-feed profile: Insuring 8-10th month
050
100150200250300350400
024681012141618
LGM Premium Paid Actual Margin
Margin with LGM Net Indemnity
$/Mg of Milk $/cwt of Milk
Why using deferred contracts works the best
Room 2: Mind your Tail
How is LGM-Dairy priced?
Jan Feb
Mar
Apr May
Jun Jul Aug
Sep
Oct Nov
Dec
Purchase at End of
Month
No Coverage
1 2 3 4 5 6 7 8 9 10
Insurance Contract Period
• Extract information regarding expected prices and volatilities from futures prices and at-the-money options
• Calculate correlations based on historical data
• Use Monte Carlo methods to simulate indemnities• Price of the Asian Basket Option set at mark-up over
actuarially fair price (e.g. expected indemnity).
17
Is correlation a good way to think about dependence between variables?
Lower tail dependence
1 20
lim Pr ,Lu
U u U u
Upper tail dependence
1 21
lim Pr ,Uu
U u U u
Copulas: Tool for dealing with nonlinear dependencies
1 2 1 1 1 1 1 1
1 2 1 1 2 2
, ,... ,..., ,
, ,... , ,...,
p p p
p p p
F x x x P X x X x F x P X x
F x x x C F x F x F x
GaussianClayton Gumbel
Comparing Copula Families
Empirical Copula
• Empirical copula replaces unknown distributions with their empirical counterparts:
• Implementation: Bootstrap based on rank-order matrix• Potential shortcomings: Small sample, serial dependency
1 1 1,..., ,...,p p pC u u F F x F x
1 1 1,..., ,...,p n n np pC u u F F x F x
1 1 11
1,..., ,...,T
n p i ip pi
F x x I X x X xT
1
1 T
np p ip pi
F x I X xT
Effect of non-linear dependence on LGM premiums
Home-Feed Market-FeedDeductible $0.00 $1.10 $0.00 $1.10
Official RMA Method $14,569 $7,380 $20,350 $13,308
Rank Correlations $14,998 $7,719 $16,439 $9,504
Empirical Copula $15,286 $8,219 $15,478 $8,246
• Unlike most situations in financial sector, in livestock margin insurance tail dependence decreases portfolio risk.
Room 3: Mr. Black, this drink is flat.
How is LGM-Dairy priced?
Jan Feb
Mar
Apr May
Jun Jul Aug
Sep
Oct Nov
Dec
Purchase at End of
Month
No Coverage
1 2 3 4 5 6 7 8 9 10
Insurance Contract Period
• Extract information regarding expected prices and volatilities from futures prices and at-the-money options
• Calculate correlations based on historical data• Use Monte Carlo methods to simulate indemnities• Price of the Asian Basket Option set at mark-up over
actuarially fair price (e.g. expected indemnity).
Are Futures Prices Unbiased?
Testing for bias in futures prices
t t Tf E p
0t Tt
t
f pEf
, ,
1 ,
1 0N
t i T i
i t i
f pN f
Test Design
22
, ,
1
1ln ln1 2 1
T i t i iN
i i
p f
N
, ,
1 ,
1 0N
t i T i
i t i
f pN f
• Essential assumption: Lognormality
Bootstrap procedure
2exp ln 0.5T t tp z f
Testing for Futures Price Bias
1 2 3 4 5 6 7 8 9-15
-10
-5
0
5
10 Class III Milk
Nearby
Pred
ictio
n Er
ror (
%)
Testing for Futures Price Bias
1 2 3 4 5
-20-15-10
-505
101520
Corn
Nearby
Pred
ictio
n Er
ror (
%)
Testing for Futures Price Bias
1 2 3 4 5 6-15
-10
-5
0
5
10
15Soybean Meal
Nearby
Pred
ictio
n Er
ror (
%)
Testing for Implied Volatility Bias
1 2 3 4 50.70.80.9
11.11.21.31.41.5
Corn
Nearby
Roo
t Mea
n Sq
uare
Sta
ndar
dize
d Pr
edic
tion
Erro
r (%
)
Testing for Implied Volatility Bias
1 2 3 4 5 60.70.80.9
11.11.21.31.41.5
Soybean Meal
Nearby
Roo
t Mea
n Sq
uare
Sta
ndar
dize
d Pr
edic
tion
Erro
r (%
)
Testing for Implied Volatility Bias
1 2 3 4 5 6 7 8 90.70.80.9
11.11.21.31.41.5
Class III Milk
Nearby
Roo
t Mea
n Sq
uare
Sta
ndar
dize
d Pr
edic
tion
Erro
r (%
)
Testing for Implied Volatility Bias
3 4 5 6 7 8 9 10 110.00
0.05
0.10
0.15
0.20
0.25
Mean Implied VolatilityLowest Average IV Consistent with Data
Nearby
Impl
ied
Vola
tility
Effect of biases on LGM premiums
Home-Feed Market-FeedDeductible $0.00 $1.10 $0.00 $1.10
Official RMA Method 9,743 5,191 13,316 8,873
Biased Soymeal Futures
9,744 13,438 5,192 8,992
Biased Milk Volatility 10,972 6,287 14,235 9,686
39
Room 4: A reason to smile.
How is LGM-Dairy priced?
Jan Feb
Mar
Apr May
Jun Jul Aug
Sep
Oct Nov
Dec
Purchase at End of
Month
No Coverage
1 2 3 4 5 6 7 8 9 10
Insurance Contract Period
• Extract information regarding expected prices and volatilities from futures prices and at-the-money options
• Calculate correlations based on historical data• Use Monte Carlo methods to simulate indemnities• Price of the Asian Basket Option set at mark-up over
actuarially fair price (e.g. expected indemnity).
Does it matter if marginal distributions are in fact not lognormal?
• In the current RMA ratings method, only at-the-money puts and calls are used to estimate variance of the terminal prices.
15%
20%
25%
30%
35%
40%
Log(Strike/Underlying Futures Price)
Implied Volatility
Date: Jun 26, 2006Contract: Corn, Dec ’06Futures Price: $2.49
2.80 3.30 3.80 4.300.30
0.32
0.34
0.36
0.38
0.40
S=0, K=3 S=0, K=3.5S=0, K=4.5 S=0, K=5.4
Strike Price
Impl
ied
Vol
atili
ty
42
Volatility smiles induced by high kurtosis
$3.00 $3.50 $4.000.30
0.32
0.34
0.36
0.38
0.40
0.42
S=0.3, K=3.5 S=0.6, K=4.5Strike Price
BS: I
mpl
ied
Vola
tility
43
Volatility skews induced by high skewness
-3 -2 -1 0 1 2 3 4 5 60.000.100.200.300.400.500.600.70
S=-1, K=6S=2, K=11S=1, K=6S=0, K=3
431
12
1p pF p
44
Generalized Lambda Distribution (GLD) allows changing one moment at a time
Scenario 1: Corn as the only source of riskCorn skewness boosted 60%
00.
20.
40.
60.
8 11.
21.
41.
61.
8 22.
22.
42.
62.
8 33.
23.
43.
63.
8 44.
24.
44.
64.
8 5
-10.00%-5.00%0.00%5.00%
10.00%15.00%20.00%25.00%30.00%
Skewness Boost
00.
20.
40.
60.
8 11.
21.
41.
61.
8 22.
22.
42.
62.
8 33.
23.
43.
63.
8 44.
24.
44.
64.
8 5
-10.00%-8.00%-6.00%-4.00%-2.00%0.00%2.00%4.00%6.00%8.00%
10.00%Kurtosis Boost
Kurtosis Boost
Scenario 2: Corn as the only source of riskCorn kurtosis boosted 60%
Scenario 3: Corn as the only source of riskBoth skewness and kurtosis boosted
00.
20.
40.
60.
8 11.
21.
41.
61.
8 22.
22.
42.
62.
8 33.
23.
43.
63.
8 44.
24.
44.
64.
8 5
-10.00%-5.00%0.00%5.00%
10.00%15.00%20.00%25.00%30.00%35.00%40.00%
Kurtosis Boost Skewness BoostSkewness & Kurtosis Boost
Scenario 4: Two sources of risk – milk and cornEffect nearly disappears
00.
20.
40.
60.
8 11.
21.
41.
61.
8 22.
22.
42.
62.
8 33.
23.
43.
63.
8 44.
24.
44.
64.
8 5
-10.00%-5.00%0.00%5.00%
10.00%15.00%20.00%25.00%30.00%35.00%40.00%
Skewness & Kurtosis BoostLognormal Milk, S&K Boost
Conclusions
• Modeling dependence using correlations may not suffice – tail dependence matters!
• Simplistic heuristics and CME settlement rules may have rendered dairy options too cheap.
• Volatility smiles may not be important for pricing Asian Basket Options
Practical Issues in Pricing (and Using) Asian Basket Options: A Case of Livestock Gross Margin Insurance
MFM SeminarSeptember 28, 2012
Dr. Marin [email protected](612) 624-4746Department of Applied EconomicsUniversity of Minnesota-Twin Cities317c Ruttan Hall1994 Buford AvenueSt Paul, MN 55108
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