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3
RD
ANNUAL
CBOE/BLOOMBERG
EQUITY VOLATILITY
SYMPOSIUM
FEBRUARY // 1 // 2012
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WELCOME TO BLOOMBERG
3:00PM OPENING REMARKS
DAVID MITCHELL, EQUITY DERIVATIVES SPECIALIST, BLOOMBERG LP
3:15PM QUANTITATIVE STRATEGIES FOR TRADING VOLATILITY
BRUNO DUPIRE, HEAD OF QUANTITATIVE RESEARCH, BLOOMBERG LP
4:15PM NEW TOOLS FOR ANALYZING VOLATILITY MARKETS
DAVID MITCHELL, EQUITY DERIVATIVES SPECIALIST, BLOOMBERG LP
5:15PM PANEL DISCUSSION
MODERATED BY PAUL STEPHENS, DIRECTOR AND DEPARTMENT HEAD, CHICAGO BOARD OPTIONS
EXCHANGE
ART CONDODINA, HEAD OF EQUITY DERIVATIVES TRADING, BNY MELLON
TREVOR MOTTL, HEAD OF MACRO AND DERIVATIVE STRATEGY, SUSQUEHANNA INTERNATIONAL GROUP
JOHN-MARK PIAMPIANO, PORTFOLIO MANAGER, PINE RIVER CAPITAL MANAGEMENT
JASON UNGAR, MANAGING DIRECTOR, GUGGENHEIM PARTNERS
6:00PM RECEPTION
2
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Comments on 2011: Volatility as an Asset
Class? Proliferation of Instruments
3
A =1 VIX track is most
liquid ETN.
Benchmark indices
ability to get exposure
vial OTCs and
Structured Notes
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Comments on 2011: Volatility as a
Source of Alpha
4
Closed End Fund is
popular vehicle for
systematic strategies
sold to retail investors.
Open end funds with
income or risk focus
often actively use
options.
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Comments on 2011: Performance of
Volatility Strategies
5
Benchmark indices that
were net sellers of
premium outperformed
collar strategy
VIX futures and
proprietary overlay
strategies had mixed
performance
Implied vs Realized
strategy hurt by Greece
scare.
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Comments on 2011: Beta Beta Beta
6
Many of the most liquid
securities in 2011 were
beta or macro related.
Impact on realized
correlation?
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Comments on 2011: Cross Asset & Inter-
Asset Correlation
7
Implied correlation remained high even
after vol decreased and prices
recovered.
Quantitative Strategies
for Trading Volatility
Bruno Dupire
Head of Quantitative Research
Bloomberg L.P.
Bloomberg Equity Volatility Symposium
New York, February 1, 2012
Known Unknowns
I know one thing, that I know nothing.
Plato
Known Unknowns
There are known knowns; there are things we know we
know.
We also know there are known unknowns; that is to say we
know there are some things we do not know.
But there are also unknown unknowns there are things
we do not know we don't know.
United States Secretary of Defense Donald Rumsfeld
Known Unknowns
Finance is the art of known unknowns, quantifying
uncertainty
Stock price moves are uncertain but we postulate
precisely how uncertain they are
Quantifying Uncertainty
Finance is the art of known unknowns
Stock prices moves are uncertain
the uncertainty is uncertain
dW dt
S
dS
o + =
o
dW dt k d q o u o + = ) (
Measures the
uncertainty
Uncertainty of
uncertainty
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
5
10
15
20
25
30
35
40
45
v
o
l a
t i l i t y
( %
)
time
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
90
95
100
105
110
115
120
125
130
time
p
r i c
e
Historical/Implied Moments
Bruno Dupire 14
Historical/implied moments
Volatility Skew: slope of implied volatility as a
function of Strike
Link with Skewness (asymmetry) of the Risk
Neutral density function ?
Moments Statistics Finance
1 Expectation FWD price
2 Variance Level of implied vol
3 Skewness Slope of implied vol
4 Kurtosis Convexity of implied vol
Bruno Dupire 15
S&P 500: Option Prices
Bruno Dupire 16
Non parametric fit
of implied vols
1000 1100 1200 1300 1400 1500 1600 1700
10
15
20
25
30
35
40
K
I
m
p
l
i
e
d
(
%
)
SPX Implied Vols on 31-Jan-2o12 (1M)
Bruno Dupire 17
Implied Volatilities
Bruno Dupire 18
Local Volatilities
Bruno Dupire 19
Risk Neutral Densities
500 750 1000 1250 1500 1750 2000
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.01
Risk Neutral Density SPX on 31-Jan-2o12 (1M, 3M, 1Y)
SPOT
P
D
F
Implied Moments
Moments from option prices
Implied vols Option prices Moments
B.S
] ) [(
n
T T n
S S E =
2
T
S
3
Historical Moments
Bruno Dupire 21
Historical Moments
Moments computed from past stock prices
Bruno Dupire 22
S
t
n
, , ,
2 1
Questions
How to compute historical moments?
How to exploit discrepancies between
implied and historical moments?
- Arbitrages/trades
- Design a product
Bruno Dupire 23
First moment: fwd
Bruno Dupire 24
0
t t i
S S x
i
t
0
t
i
t
i+1
i i
t t i
S S
+1
o
i i i
x x o =
+1
1 = n
=
=
1
0
N
i
i N
x o
Second moment: variance
Variance swaps primer (absolute version)
Bruno Dupire 25
2 2 2
1
2
i i i i i
x x x o o + =
+
2 = n
=
=
+ =
1
0
2
1
0
2
2
N
i
i
N
i
i i N
x x o o
Summing:
hedge A
realized
variance
Third moment: Skewness
Bruno Dupire 26
3 2 2 3 3
1
3 3
i i i i i i i
x x x x o o o + + =
+
3 = n
=
=
+ + =
1
0
3
1
0
2
1
0
2 3
3 3
N
i
i
N
i
i i
N
i
i i N
x x x o o o
Summing:
hedge A
leverage daily skewness
Leverage dominates the daily skewness by far
Dissociating Daily Skewness &
Leverage
2005 2006 2007 2008 2009 2010 2011
-10
-8
-6
-4
-2
0
2
x 10
7
S&P500 (with 3m time window)
leverage
daily skew
leverage + daily skew
Dissociating Daily Skewness &
Leverage
Bruno Dupire 28
S&P500 (with 3m time window)
1985 1986 1987 1988 1989 1990
-14
-12
-10
-8
-6
-4
-2
0
2
x 10
5
leverage
daily skew
leverage + daily skew
Fourth moment: Kurtosis
Bruno Dupire 29
4 3 2 2 3 4 4
1
4 6 4
i i i i i i i i i
x x x x x o o o o + + + =
+
4 = n
=
=
+ + + =
1
0
4
1
0
3
1
0
2 2
1
0
3 4
4 6 4
N
i
i
N
i
i i
N
i
i i
N
i
i i N
x x x x o o o o
Summing:
hedge A
Tail variance conditional
skewness
daily
kurtosis
Tail variance dominates the conditional skewness and daily
kurtosis by far
Historical Fourth Moment
2005 2006 2007 2008 2009 2010 2011
-1
0
1
2
3
4
5
6
7
8
x 10
10
S&P500 (with 3m time window)
daily kurtosis
tail var
cond skew
kurtosis + tail var + cond skew
Historical Fourth Moment
Bruno Dupire 31
1985 1986 1987 1988 1989 1990
-0.5
0
0.5
1
1.5
2
2.5
x 10
8
daily kurtosis
tail var
cond skew
kurtosis+tail var+cond skew
S&P500 (with 3m time window)
Jumps
Negative implied skewness
What about daily skewness?
Bruno Dupire 32
Are big moves down?
Bruno Dupire 33
2008 2009 2010
600
700
800
900
1000
1100
1200
1300
1400
1500
Two moves of more
than 10%, both up!
Close up
Bruno Dupire 34
Aug-01-2008 31-Dec-2008
700
800
900
1000
1100
1200
1300
1400
Two moves of more
than 10%, both up!
Biggest historical returns
Bruno Dupire 35
Dates Returns
10/28/1929 - 12.94%
10/30/1929 12.53%
06/22/1931 10.51%
10/06/1931 12.36%
09/21/1932 11.81%
03/15/1933 16.61%
09/05/1939 11.86%
10/19/1987 - 20.47%
10/13/2008 11.58%
10/28/2008 10.79%
Over the last 100 years, top 10 returns, 8 out of 10 up!
Levy models
Explain negative skews by iid jumps with negative
skewness
iid assumption makes little sense
CLT washes away skewness
Daily skewness negligible compared to Leverage
Historical daily skewness not clearly negative
Bruno Dupire 36
Theoretical skew
from price history
Bruno Dupire 38
Theoretical Skew from Prices
Problem : How to compute option prices on an underlying without
options?
For instance : compute 3 month 5% OTM Call from price history only.
1) Discounted average of the historical payoffs.
Bad : depends on bull/bear, no call/put parity.
2) Generate paths by sampling 1 day return recentered histogram.
Problem : CLT => converges quickly to same volatility for all
strike/maturity; breaks autocorrelation and vol/spot dependency.
?
=>
Bruno Dupire 39
Theoretical Skew from Prices (2)
3) Discounted average of the Intrinsic Value from recentered 3 month
histogram.
4) -Hedging : compute the implied volatility which makes the -
hedging a fair game.
Bruno Dupire 40
Strike dependency
Fair or Break-Even volatility is an average of squared
returns, weighted by the Gammas, which depend on the
strike
Bruno Dupire 41
Strike dependency for multiple paths
Bruno Dupire 42
Theoretical Skew
from historical prices
S&P500 2006
Bruno Dupire 43
80
90
100
110
120 1m
2m
3m
6m
1y
18m
2y
0
0.2
0.4
0.6
0.8
1
maturity
SP500 1/3/2006 to 1/3/2009
moneyness
BEVL surface
1/3/2006 Implied vol surface
S&P500 2008
Bruno Dupire 44
80
90
100
110
120
1m
2m
3m
6m
1y
18m
2y
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
maturity
SP500 10/1/2008 to 11/22/2010
moneyness
BEVL surface
10/1/2008 Implied vol surface
Conditional Realized Variance
Bruno Dupire 45
variance realized historical ion to approximat apply this We
] [ ) , (
summary In
) , ( ) ], [ (
] [ ] [ ] [
that follows It
) ], [ ( ] ] [ [ ] [
then , Assume
2
2
0
0 0
'
K S RV E T T K
T T K dt t K S S E v
dt K S v E K S dt v E K S RV E
t K S S E v K S S E S v E K S v E
dW v dS
T T impl
impl
T
T t loc
T
T
t T
T
t T T
T t loc T t t t T t
t t t
= ~
~ = =
= = = = =
= = = = ~ =
=
}
} }
o
o
85 90 95 100 105 110
0
0.02
0.04
0.06
0.08
0.1
0.12
Final Return
R
e
a
l
i
z
e
d
V
a
r
i
a
n
c
e
Realized Variance vs Final Return
S&P500 example
S&P500 example
85 90 95 100 105 110
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Final Return
R
e
a
l
i
z
e
d
V
o
l
Realized Vol vs Final Return
Density extracted from previous skew
85 90 95 100 105 110
0
0.02
0.04
0.06
0.08
0.1
0.12
Implied/Realized Skewness
Strategies
One period skewness/dynamic leverage
Compute volatility adjusted historical skewness
If less negative than implied skewness
- Buy a cubic profile
- Perform the Delta hedge
It captures the excess volatility of the Puts over the
Calls
Bruno Dupire 50
Time diversification
Over each time period
- Buy a cubic profile or risk reversals
- Receive/pay the payoff at the end of the period
With negative implied skewness, the premium is
negative (receive $)
Average payoff should follow the historical
skewness
Bruno Dupire 51
Asset diversification
Multi stock skewness
To speed up this lengthy strategy, products
exploit the skewness of the cross sectional
returns of several stocks
It is an attempt to replace the time diversification
by an asset diversification
Bruno Dupire 52
Skewness Products
Bruno Dupire 53
Negative Skewness: fat tail to the left
Linked to UP Dispersion < Down Dispersion
+ = + =
= =
=
=
n
n i
n
n j
j
j
T
i
i
T
n
i
n
j
j
j
T
i
i
T
S
S
S
S
n
S
S
S
S
n
1 2 / 1 2 /
0 0
2 /
1
2 /
1
0 0
2 /
1
Dispersion Down
2 /
1
Dispersion Up
Some products are based on
Up Dispersion Down Dispersion
Hedge with Components
Gaussian returns: Decorrelated Case
Good hedge with risk reversals on the
components
Bruno Dupire 54
Hedge with Components
Correlated Case
No hedge with components
Bruno Dupire 55
Analysis
Correlation shifts the returns, which affects the
components hedge but not the target
In the absence of hedge, it is a mistake to price the
product with a calibrated model
Implied individual skewness is irrelevant !
It is a wrong way to play implied versus realized
skewness
Bruno Dupire 56
Up Dispersion
Bruno Dupire 57
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
-1
0
1
2
3
4
5
Return
H
e
d
g
e
Up Variance
Bruno Dupire 58
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
-1
0
1
2
3
4
5
6
7
8
9
Return
H
e
d
g
e
Up Standard Deviation
Bruno Dupire 59
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
-1
0
1
2
3
4
5
6
7
8
Return
H
e
d
g
e
BMW
Sample Mean of Best third
+
Sample Mean of Worst third
-2 x
Sample Mean of Middle third
BMW
M B W
Return
BMW with 30,000 stocks, Correlation = 0%
Hedge with Puts and Calls using 120 strikes
R-squared = 100%
-3 -2 -1 0 1 2 3
-6
-4
-2
0
2
4
6
Level
P
a
y
o
f
f
Component Hedge
Norm Cdf = 2/3
Norm Cdf = 1/3
BMW with 30,000 stocks, Correlation = 0%
Hedge with Puts and Calls using 120 strikes
R-squared = 100%, MC runs = 10,000
-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
Hedge
T
a
r
g
e
t
Perfect Hedge for BMW, Correlation = 0%
Short 6 Shares, Short 9 Puts at K*, Long 9 Calls at K*
-3 -2 -1 0 1 2 3
-6
-4
-2
0
2
4
6
Level
P
a
y
o
f
f
Component Hedge
Norm Cdf = 2/3
Norm Cdf = 1/3
K* = 0.4307
BMW with 30 stocks , Correlation = 0
Hedge with Puts and Calls using 120 strikes
R-squared = 80.7%, MC runs = 10,000
-1.5 -1 -0.5 0 0.5 1
-1
-0.5
0
0.5
1
1.5
Hedge
T
a
r
g
e
t
BMW with 30 stocks , Correlation = 40%
Hedge with Puts and Calls using 120 skrikes
R-squared = 5.8%, MC runs = 10,000
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Hedge
T
a
r
g
e
t
Sensitivity to Correlation
Payoff: Second best out of 3 stock returns
1)
Second best: worst of A and B, long correlation
2)
Second best: worst of B and C, short correlation
BMW: B + W 2M = B + M + W 3M
C B A
C B A
Linear Second best
Summary
Skewness comes from leverage, not from daily
skewness
Daily skewness not so negative
Levy models not appropriate to model skewness
It is dangerous to replace time diversification by
asset diversification
Bruno Dupire 68
FX Games
Bruno Dupire 70
FX Triangle Arbitrage
USD EUR X / USD JPY Y / JPY EUR Z /
t
t
t
Y
X
Z =
Y X Y X Y X Z
o o o o o o 2
2 2 2
/
2
+ = =
Y X Z Y X
o o o o o + s s
Spot arbitrage:
Vol arbitrage:
A
B
Z
C
Y
X
Implemented by:
+ + +
+ s ) ( ) ( ) (
0 0 0 0
Y Y Z X X Y Z X
FX Triangle
In general
+ + +
+ s ) ( ) ( ) (
0 0 0 0
Y Y Z X X Y Z X
ATM short maturity
approximation
y x z
o o o + s
) ln( X x = ) ln( Y y = 1 = + | o
2 2 2 2
) ( ) ( x y y x y x + = + | o | o | o
RHS replicable by options (up to a quanto effect
which vanishes for small maturities)
USD EUR X /
USD JPY Y /
JPY EUR Z /
A
B
Z
C
Y
X
FX Triangle Arbitrage
10 10
18
If close to arbitrage, RHS is a cheap way to
replicate LHS, or after
delta hedge
) ( of QV y x | o +
2 2 2 2
) ( ) ( x y y x y x + = + | o | o | o
Bruno Dupire 73
n-Dim Currency Case
Bruno Dupire 74
Tetrahedron Arbitrage
With 4 currencies, all triangles may be viable but still
there is a global arbitrage
In general, it is possible to trade the height of a simplex
A
C
B
D
18 18
18
10
10 10
10
10 10
Bruno Dupire 75
n-Dim Arbitrage
= < =
s
n
i
i i
n
i j
j i j i
n
i 1
2
0 ,
2
,
1
o o o o o
< = = = =
|
.
|
\
|
=
n
i j
j i j i
n
i
n
i
i i
n
i
i
n
i
i i
X X X X
2
1
2
1 1
2
1
o o o o o
1
1
=
=
n
i
i
o
The difference is minimized by
with
1 ' 1
1
1
1
=
V
V
o
as
) , (
, j i j i
X X Cov V
LHS of identity above represents the
If the simplex is too flat, RHS is a cheap way to capture the QV of LHS
Buy VS on straight pairs and sell VS on crosses (short maturity to cancel
the quanto effect)
2
simplex) of (height
Historical / Implied vol arbitrage
better? do Can we
pair optimal the find can We
strategy vol
/implied historical the of profit the is | ' |
' variance implied and e varianc
historical with pairs 2 1 currencies N
* *
,
-
-
-
-
j i
ij
ij
ij
ij
ij i
A
- v v
v v
A )/ n(n- A
Optimal Trade
pair any of an greater th is | |
8 4
2
D C
2
B A
6 4
Let
4 2 4
2 ' ' where
: n Tetrahedro Implied
: n Tetrahedro Historical
2
0
0
0
0
' '
2 2 2
2 2 2 2
0
0
0 0
o o
c o
| o
c o o o o c o o
o
c c
o|
o| o|
-
= =
|
|
.
|
\
|
=
+
|
|
.
|
\
|
=
+
= = = = = =
+ = = = = = =
+ = =
-
|
|
.
|
\
|
|
|
.
|
\
|
|
|
.
|
\
|
|
|
.
|
\
|
-
y v
v v
y x v v v v x v v
y y x x
D' C' B' A'
D C B A
y y
BD BC AD AC CD AB
AB B A AB
BD BC AD AC CD AB
y
x
y
x
y
x
y
x
A Few Tools
79
80
Price Dispersion
0 1 2 3 4 5 6 7 8 9 10
-10
-5
0
5
10
15
Time
D
o
l
l
a
r
P
&
L
P&L Attribution
spot P&L
vol P&L
rate P&L
time P&L
Delta Hedged Position
0 1 2 3 4 5 6 7 8 9 10
-5
-4
-3
-2
-1
0
1
Time
D
o
l
l
a
r
P
n
L
Performance Attribution
gamma PnL
theta PnL
vega PnL
vanna PnL
volga PnL
Vol/Return Analysis
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NEW TOOLS FOR
ANALYZING
VOLATILITY
MARKETS
>>>>>>>>>>>>>>
DAVID MITCHELL
EQUITY DERIVATIVES SPECIALIST, BLOOMBERG LP
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AGENDA
BETA
RELATIVE VALUE
REALTIME VOLATILITY ANALYSIS
CORRELATION
86
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Carbon Analysis Tool Carbon Analysis Tool
87
BETA: FAIRFUTURES ROLL AND FAIR
VALUE ANALYTICS
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Carbon Analysis Tool Carbon Analysis Tool
88
BETA: FAIRFUTURES ROLL AND FAIR
VALUE ANALYTICS
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Carbon Analysis Tool Carbon Analysis Tool
89
BETA: BTMBLOCK TRADE MONITOR FOR
FUTURES & OPTIONS IN ONE PLACE
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Quickly view absolute and comparative volatility metrics on index
constituents or custom ticker lists.
Tabs available for Index, Commodity and Currency volatility data
Carbon Analysis Tool Carbon Analysis Tool
90
RELATIVE VALUE: VCAVOLATILITY
DASHBOARD
Example workflow: Use VCA to see that SKEW is high on many names in
the OEX
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RELATIVE VALUE: OSCHOSA
SEARCH FOR CHEAP RISK REVERSALS
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Carbon Analysis Tool Carbon Analysis Tool
92
RELATIVE VALUE: OSCHOSA
SEARCH FOR CHEAP RISK REVERSALS
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Carbon Analysis Tool Carbon Analysis Tool
93
RELATIVE VALUE: OVMECHEAP
INSURANCE VIA D&O PUTS
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Carbon Analysis Tool Carbon Analysis Tool
94
RELATIVE VALUE: VCAVOLATILITY
DASHBOARD
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Carbon Analysis Tool Carbon Analysis Tool
95
RELATIVE VALUE: VCAVOLATILITY
DASHBOARD
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Carbon Analysis Tool Carbon Analysis Tool
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RELATIVE VALUE: VCAVOLATILITY
DASHBOARD
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REALTIME: GIVINTRA-DAY CHANGES IN
IMPLIED VOLATILITY (TICKING)
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Carbon Analysis Tool Carbon Analysis Tool
98
REALTIME: BORROW COST ANALYSIS:
IN REALTIME IN OMON
Parity
Forward
New
Implied Vol
Implied
borrow cost
Implied
basis pts
Implied
basis yield
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Carbon Analysis Tool Carbon Analysis Tool
99
REALTIME: XLTP/OPTIONS: READY TO
EAT OPTIONS SPREADSHEETS
World Equity Indices Sort on: Unsorted Sort using: ATM +/- 2 Strikes Currency: USD
Show above: 0 % DMA Exercise: European Average Volume 30D
I NDU DOW JONES I NDU AVG 127.32 2/18/2012 128 0.1 6,531,516 17 5 2/16/2012 4150.000 1,082 321,356,923.71
SPX S&P 500 I NDEX 1,317.81 2/18/2012 1,325 3.1 3,587,605,944 6,130 1,120 2/16/2012 4200.000 752 223,402,723.75
NDX NASDAQ 100 STOCK I NDX 2,453.14 2/18/2012 2,500 10.0 926,796,292 1,503 332 AS51 S&P/ASX 200 INDEX 4271.340 2/16/2012 4250.000 782 232,340,647.51
I BOV BOVESPA I NDEX FUT Feb12 63,210.00 2/16/2012 65,000 27.9 663,555,666 0 0 2/16/2012 4300.000 1,096 325,485,609.31
SPTSX60 S&P/TSX 60 I NDEX 710.39 2/17/2012 710 0.6 10,355,099 36 7 2/16/2012 4350.000 1,279 379,839,074.59
MEXBOL MEX BOLSA I DX FUT Mar12 37,425.00 3/16/2012 37,000 52.1 27,391,268 #VALUE! #VALUE!
SX5E Euro Stoxx 50 Pr 2,460.40 2/17/2012 2,500 18.0 4,977,629,040 51,599 11,572
UKX FTSE 100 I NDEX 5,795.20 2/17/2012 5,750 5.2 406,169,515 2,723 595
CAC CAC 40 I NDEX 3,363.23 2/17/2012 3,400 19.9 186,349,646 2,727 402
DAX DAX I NDEX 6,539.85 2/17/2012 6,500 3.2 824,779,510 1,839 363
I BEX I BEX MI NI I DX FUT Feb12 8,703.00 2/17/2012 8,500 3.5 3,786,553 110 21
FTSEMI B FTSE MI B I NDEX 16,111.04 2/17/2012 16,500 11.2 160,568,304 403 76
AEX AEX-I ndex 322.82 2/17/2012 330 28.3 171,760,578 996 225
OMX OMX STOCKHOLM30 I NDEX 1,046.01 2/17/2012 1,020 36.6 200,774,536 2,558 675
SMI SWI SS MARKET I NDEX 6,100.43 2/17/2012 6,000 35.2 670,893,484 3,337 1,070
NKY NI KKEI 225 8,849.47 2/10/2012 9,000 76.4 3,441,109,330 1,569 703
HSI HANG SENG I NDEX 20,439.14 1/30/2012 20,000 8.6 408,558,910 398 373
AS51 S&P/ASX 200 I NDEX 4,271.34 2/16/2012 4,350 1,278.5 379,839,075 752 236
This spreadsheet requires installation of the 'Bloomberg Options Tools'
Add-in to function correctly. To install click here to download, then
press the install button on the open spreadsheet.
Thi s page di spl ays opti ons havi ng hi gh open i nterest for major equi ty i ndi ces.
Sel ect sorti ng and fi l teri ng opti ons above.
Cl i ck on any l i ne i n the tabl e bel ow to sel ect i t and vi ew the i ndex i n more detai l i n the ri ght hand pane
Name Stri ke Ti cker Pri ce Expi ry Expi ry
Noti onal amount
exerci sabl e
Ti cker Name Pri ce
%
exerci sabl
e of 30
dma
Stri ke
% exerci sabl e
of 30 dma
Noti onal amount
exerci sabl e
Gamma
1% move
# Futures
Current
Del ta
# Futures
Gamma Finder
0 200 400 600 800 1,000 1,200 1,400
4150
4200
4250
4300
4350
% ADV
S
t
r
i
k
e
Calls
Puts
4350
OMON IGPV BTM GIPT
Help
GIV
Install Options Tools Addin
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Carbon Analysis Tool Carbon Analysis Tool
100
REALTIME: EXCEL APISUPPORT FOR
WEEKLY OPTIONS IN BB PLUS
SPX INDEX 1317.03
Get a list of: Arguments
5 Tickers PTS=5
for expiry ALL EXP=ALL
strike set to At the Money STR=A
Starting with POS=T
of exercise type All EXS=ALL
using expiration periodicity All PER=ALL
of type Put PCF=P
with padding set to Yes PAD=OFF
BBOPT() SPXW US 01/27/12 P1315 Index
SPXW US 02/03/12 P1315 Index
SPX US 02/18/12 P1315 Index
SPX US 03/17/12 P1315 Index
SPXQ US 03/30/12 P1325 Index
SPX US 04/21/12 P1320 Index
SPX US 05/19/12 P1325 Index
SPX US 06/16/12 P1325 Index
SPXQ US 06/29/12 P1325 Index
SPX US 09/22/12 P1325 Index
SPXQ US 09/28/12 P1325 Index
SPX US 12/22/12 P1325 Index
SPXQ US 12/31/12 P1325 Index
SPX US 06/22/13 P1325 Index
SPX US 12/21/13 P1325 Index
SPX US 12/20/14 P1325 Index
BB Plus allows for
a flexible way to
pulls options
tickers and
volatility date into
spreadsheets for
customized
analysis.
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Carbon Analysis Tool Carbon Analysis Tool
101
REALTIME: VOLATILITY TICKERSACCESS
EACH POINT ON THE OVDV VOLATILITY
SURFACE
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Carbon Analysis Tool Carbon Analysis Tool
102
REALTIME: VOLATILITY TICKERS
LEVERAGE CHARTING TOOLS LIKE GV & G.
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Carbon Analysis Tool Carbon Analysis Tool
103
REALTIME: VARIANCE TICKERSUSE FLDS
TO GET TICKERS ACROSS THE TERM
STRUCTURE
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Carbon Analysis Tool Carbon Analysis Tool
104
CORRELATION: PROXY HEDGE
ANALYSIS USING GV
Is the Aussie Dollar
ETF a effective but
cheaper hedge
instrument for
equity market
events?
Correlation went
up during distress
period
Volatility on FXA
persistently lower
than SPX
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Carbon Analysis Tool Carbon Analysis Tool
105
CORRELATION: ACCESS OUR ADVANCED
NEW METHODOLOGY IN OVSN
Rolling Window
or Spot
correlation
calculation
methodology.
Worst of Oil ETF or
Bearish S&P ETF as
cheap hedge on Middle
East event?
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THE HOME PAGE FOR VOLATILITY
EDRV <GO>; BDRV <GO>
CHICAGO BOARD OPTIONS EXCHANGE
p. 107
Copyright 2012 Chicago Board Options Exchange, Incorporated. All rights reserved.
3rd Annual CBOE/Bloomberg
Equity Index Option and Volatility Symposium
New York, February 1, 2012
2012 Equity Index Option and Volatility Market Panel
Moderated by Paul Stephens, CBOE
CHICAGO BOARD OPTIONS EXCHANGE
p. 108
CBOE NEWS
New Products
SPXPM <index> OMON <go>
VXEEM <index> OMON <go>
CBOE Risk Management Conference in Florida
March 11 13, 2012
See www.cboeRMC.com
CHICAGO BOARD OPTIONS EXCHANGE
p. 109
Moderator:
Paul Stephens, CBOE
Panelists:
Art Condodina, BNY Mellon
Trevor Mottl, Susquehanna International Group
John-Mark Piampiano, Pine River Capital Management
Jason Ungar, Guggenheim Partners
2012 Equity Index Option
and Volatility Market Panel
CHICAGO BOARD OPTIONS EXCHANGE
p. 110
Options involve risk and are not suitable for all investors. Prior to buying or selling an option, a person must receive a copy of Characteristics and Risks of
Standardized Options. Copies are available from your broker, by calling 1-888-OPTIONS, or at www.theocc.com. Supporting documentation for any claims,
comparisons, statistics or other technical data in this presentation is available by calling 1-888-OPTIONS, or contacting CBOE at www.cboe.com/Contact. The
information in this presentation is provided solely for general education and information purposes. No statement within this presentation should be construed
as a recommendation to buy or sell a security or to provide investment advice. The CBOE S&P 500 BuyWrite Index (BXM), CBOE S&P 500 2% OTM
BuyWrite Index (BXY), CBOE DJIA BuyWrite Index (BXD), CBOE Russell 2000 BuyWrite Index (BXR), CBOE NASDAQ-100 BuyWrite Index (BXN), CBOE
S&P 500 PutWrite Index (PUT), and CBOE S&P 500 95-100 Collar Index (CLL) (the Strategy Indexes) are designed to represent proposed hypothetical
options strategies. Like many passive benchmarks, the Strategy Indexes do not take into account significant factors such as transaction costs and taxes.
Transaction costs and taxes for buy-write, put-write and collar strategies could be significantly higher than transaction costs for a passive strategy of buying-
and-holding stocks. Investors attempting to replicate the Strategy Indexes should discuss with their brokers possible timing and liquidity issues. Past
performance does not guarantee future results. This presentation contains comparisons, assertions, and conclusions regarding the performance of indexes
based on backtesting, i.e., calculations of how the indexes might have performed in the past if they had existed. Backtested performance information is purely
hypothetical and is provided in this presentation solely for informational purposes. The methodologies of the Strategy Indexes and CBOEs Volatility Indexes
are owned by Chicago Board Options Exchange, Incorporated (CBOE) and may be covered by one or more patents or pending patent applications. Standard
& Poor's, S&P, and S&P 500 are registered trademarks of Standard & Poors Financial Services, LLC and are licensed for use by CBOE. Standard &
Poor's does not promote, market, sell or endorse any product based upon its indices. "Dow Jones", "The Dow", "DJIA" and Dow Jones Industrial Average
are trademarks of Dow Jones & Company, Inc. and have been licensed for use for certain purposes by CBOE. CBOE's options based on Dow Jones indexes
and financial products based on the CBOE DJIA BuyWrite Index are not sponsored, endorsed, marketed or promoted by Dow Jones and Dow Jones makes
no representations regarding the advisability of investing in such products. Nasdaq, Nasdaq-100, and Nasdaq-100 Index, are trademarks of The Nasdaq
Stock Market, Inc. (which with its affiliates is referred to as the "Corporations") and are licensed for use by CBOE. The CBOE NASDAQ-100 BuyWrite Index is
not derived, maintained, published, calculated or disseminated by the Corporations. Russell 2000 is a registered trademark of the Frank Russell Company,
used under license. CBOE Volatility Index, VIX, CBOE, Chicago Board Options Exchange, CFLEX, FLEX, FLexible EXchange Hybrid and
LEAPS are registered trademarks and BuyWrite, BXM, BXY, CFE, CLL, EVZ, GVZ, OVX, PULSe, PUT, RVX, VXD, VXN, VXO and Weeklys are service
marks of CBOE. C2 and SPXpm are service marks of C2 Options Exchange, Incorporated. All other trademarks and servicemarks are the property of their
respective owners. Copyright 2012 Chicago Board Options Exchange, Incorporated. All Rights Reserved.
Disclaimers
CHICAGO BOARD OPTIONS EXCHANGE
p. 111
Contact Information:
Paul Stephens, 1-312-786-7495, stephens@cboe.com
Institutional and International Marketing Department: 1-312-786-8310, institutional@cboe.com
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3
RD
ANNUAL
CBOE/BLOOMBERG
EQUITY VOLATILITY
SYMPOSIUM
FEBRUARY // 1 // 2012
