model free results on volatility derivatives
DESCRIPTION
Model Free Results on Volatility Derivatives. Bruno Dupire Bloomberg NY SAMSI Research Triangle Park February 27, 2006. Outline. I VIX Pricing II Models III Lower Bound IV Conclusion. Historical Volatility Products. Historical variance: OTC products: Volatility swap Variance swap - PowerPoint PPT PresentationTRANSCRIPT
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Model Free Results onVolatility Derivatives
Bruno DupireBloomberg NY
SAMSI Research Triangle Park
February 27, 2006
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Bruno Dupire 2
Outline
• I VIX Pricing
• II Models
• III Lower Bound
• IV Conclusion
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Bruno Dupire 3
Historical Volatility Products
• Historical variance:
• OTC products:– Volatility swap
– Variance swap
– Corridor variance swap
– Options on volatility/variance
– Volatility swap again
• Listed Products– Futures on realized variance
n
i i
i
S
S
n 1
2
1
))(ln(1
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Bruno Dupire 4
Implied Volatility Products
• Definition– Implied volatility: input in Black-Scholes formula to recover
market price:
– Old VIX: proxy for ATM implied vol
– New VIX: proxy for variance swap rate
• OTC products– Swaps and options
• Listed products– VIX Futures contract
– Volax
MarketTK
impl CTKrSBS ,),,,,(
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I VIX Futures Pricing
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Bruno Dupire 6
Vanilla OptionsSimple product, but complex mix of underlying and volatility:
Call option has :
Sensitivity to S : Δ
Sensitivity to σ : Vega
These sensitivities vary through time, spot and vol :
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Bruno Dupire 7
Volatility GamesTo play pure volatility games (eg bet that S&P vol goes up, no view on the S&P itself):
Need of constant sensitivity to vol;
Achieved by combining several strikes;
Ideally achieved by a log profile : (variance swaps)
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Bruno Dupire 8
Log ProfileT
S
SE T
20
2
ln
dKK
KSdK
K
SK
S
SS
S
S
S
S
0
0
20
20
0
0
)()()ln(
dKK
CdK
K
PFutures
S
TKS
TKS
0
0
0 2,
02,1
Under BS: dS = σS dW,
For all S,
The log profile is decomposed as:
In practice, finite number of strikes CBOE definition:2
02
112 )1(1
),(2
2
K
F
TTKXe
K
KK
TVIX i
rT
i
iit
Put if Ki<F,
Call otherwiseFWD adjustment
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Bruno Dupire 9
Option prices for one maturity
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Bruno Dupire 10
Perfect Replication of 2
1TVIX
1
1
1ln
22
T
TTtT S
Sprice
TVIX
00
11 ln2
ln2
S
S
TS
S
Tprice TTT
t
We can buy today a PF which gives VIX2T1 at T1:
buy T2 options and sell T1 options.
PFpricet
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Bruno Dupire 11
Theoretical Pricing of VIX Futures FVIX before launch
• FVIXt: price at t of receiving at
T1 .
VIXTTT FVIXPF
111
)(][][ UBBoundUpperPFPFEPFEF tTtTtVIXt
•The difference between both sides depends on the variance of PF (vol vol), which is difficult to estimate.
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Bruno Dupire 12
Pricing of FVIX after launchMuch less transaction costs on F than on PF (by a factor of at least 20)
Replicate PF by F
instead of F by PF!
][][])[(111
22 VIXTt
VIXTt
VIXTtt FVarFEFEPF
UBPFFVarPFFEF tVIXTtt
VIXTt
VIXt ][][
11
VIXFTt
VIXs
T
t
VIXt
VIXs
VIXt
VIXTT QVdFFFFFPF
1
1
11 ,22 )(2)()(
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Bruno Dupire 13
Bias estimation][
1
2 VIXTt
VIXt FVarUBF
][1T
FVar can be estimated by combining the historical volatilities of F and Spot VIX.
Seemingly circular analysis :
F is estimated through its own volatility!
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Bruno Dupire 14
VIX Fair Value Page
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Bruno Dupire 15
Behind The Scene
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Bruno Dupire 16
VIX SummaryVIX Futures is a FWD volatility between future dates T1 and T2.
Depends on volatilities over T1 and T2.
Can be locked in by trading options maturities T1 and T2.
2 problems :
Need to use all strikes (log profile)
Locks in , not need for convexity adjustment and dynamic hedging.
2
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Bruno Dupire 17
II Volatility Modeling
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Bruno Dupire 18
Volatility Modeling• Neuberger (90): Quadratic variation can be replicated by delta
hedging Log profiles
• Dupire (92): Forward variance synthesized from European options. Risk neutral dynamics of volatility to fit the implied vol term structure. Arbitrage pricing of claims on Spot and on vol
• Heston (93): Parametric stochastic volatility model with quasi closed form solution
• Dupire (96), Derman-Kani (97): non parametric stochastic volatility model with perfect fit to the market (HJM approach)
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Bruno Dupire 19
Volatility Modeling 2
• Matytsin (99): Parametric stochastic volatility model with jumps to price vol derivatives
• Carr-Lee (03), Friz-Gatheral (04): price and hedge of vol derivatives under assumption of uncorrelated spot and vol increments
• Duanmu (04): price and hedge of vol derivatives under assumption of volatility of variance swap
• Dupire (04): Universal arbitrage bounds for vol derivatives under the sole assumption of continuity
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Bruno Dupire 20
Variance swap based approach(Dupire (92), Duanmu (04))
• V = QV(0,T) is replicable with a delta hedged log profile (parabola profile for absolute quadratic variation)
– Delta hedge removes first order risk
– Second order risk is unhedged. It gives the quadratic variation
• V is tradable and is the underlying of the vol derivative, which can be hedged with a position in V
• Hedge in V is dynamic and requires assumptions on
][][ ,.0 Tttttt QVEQVVEV
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Bruno Dupire 21
Stochastic Volatility Models
• Typically model the volatility of volatility (volvol). Popular example: Heston (93)
• Theoretically: gives unique price of vol derivatives (1st equation can be discarded), but does not provide a natural unique hedge
• Problem: even for a market calibrated model, disconnection between volvol and real cost of hedge.
ttt
t dWvS
dS
tttt dZvdtvvdv )(
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Bruno Dupire 22
Link Skew/Volvol
• A pronounced skew imposes a high spot/vol correlation and hence a high volvol if the vol is high
• As will be seen later, non flat smiles impose a lower bound on the variability of the quadratic variation
• High spot/vol correlation means that options on S are related to options on vol: do not discard 1st equation anymore
From now on, we assume 0 interest rates, no dividends and V is the quadratic variation of the price process (not of its log anymore)
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Bruno Dupire 23
Skewvolvol
LBvolvolmovesmovesS
SSkew
0
0
0),(0
To make it simple:
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Bruno Dupire 24
Carr-Lee approach• Assumes
– Continuous price
– Uncorrelated increments of spot and of vol
• Conditionally to a path of vol, X(T) is normally distributed, (g: normal sample)
• Then it is possible to recover from the risk neutral density of X(T) the risk neutral density of V
• Example:
• Vol claims priced by expectation and perfect hedge
• Problem: strong assumption, imposes symmetric smiles not consistent with market smiles
• Extensions under construction
][][])[( 222
0n
nnnn
T VEgVEXXE
gVX 0
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Bruno Dupire 25
III Lower Bound
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Bruno Dupire 26
Spot Conditioning
• Claims can be on the forward quadratic variation
• Extreme case: where is the instantaneous variance at T
• If f is convex,
Which is a quantity observable from current option prices
)),((])]|[([)]]|([[)]([ TKvfEKXvEfEKXvfEEvfE locTTTTT
21 ,TTQV
)( Tvf Tv
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Bruno Dupire 27
X(T) not normal => V not constant
• Main point: departure from normality for X(T) enforces departure from constancy for V, or:
smile non flat => variability of V
• Carr-Lee: conditionally to a path of vol, X(T) is gaussian
• Actually, in general, if X is a continuous local martingale
– QV(T) = constant => X(T) is gaussian
– Not: conditional to QV(T) = constant, X(T) is gaussian
– Not: X(T) is gaussian => QV(T) = constant
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Bruno Dupire 28
The Main Argument
• If you sell a convex claim on X and delta hedge it, the risk is mostly on excessive realized quadratic variation
• Hedge: buy a Call on V!
• Classical delta hedge (at a constant implied vol) gives a final PL that depends on the Gammas encountered
• Perform instead a “business time” delta hedge: the payoff is replicated as long as the quadratic variation is not exhausted
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Bruno Dupire 29
Trader’s Puzzle
• You know in advance that the total realized historical volatility over the quarter will be 10%
• You sell a 3 month Put at 15% implied
• Are you sure you can make a profit?
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Bruno Dupire 30
Answers• Naïve answer: YES
Δ hedging with 10% replicates the Put at a lower cost Profit = Put(15%) - Put(10%)
• Classical answer: NOBig moves close to the
strike at maturity incur
losses because Γ<< 0.
• Correct answer: YESAdjust the Δ hedge according to realized volatility so far
Profit = Put(15%) – Put(10%)
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Bruno Dupire 31
Delta Hedging
• Extend f(x) to f(x,v) as the Bachelier (normal BS) price of f for start price x and variance v:
with f(x,0) = f(x)
• Then,
• We explore various delta hedging strategies
dyeyf
vXfEvxf v
xyvx 2
)(,
2
)(2
1)]([),(
),(2
1),( vxfvxf xxv
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Bruno Dupire 32
Calendar Time Delta Hedging• Delta hedging with constant vol: P&L depends on the path
of the volatility and on the path of the spot price.
• Calendar time delta hedge: replication cost of
• In particular, for sigma = 0, replication cost of
)(2
12
1)).(,(
2,0
,022
dtdQVfdXf
dQVfdtfdXftTXdf
txxtx
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uxx dudQVfTXf0
2,0
20 )(
2
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)).(,( 2 tTXf t
t
uxxdQVfXf0 ,00 2
1)(
)( tXf
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Bruno Dupire 33
Business Time Delta Hedging
• Delta hedging according to the quadratic variation: P&L that depends only on quadratic variation and spot price
• Hence, for
And the replicating cost of is
finances exactly the replication of f until
txtxxtvtxtt dXfdQVfdQVfdXfQVLXdf ,0,0,0 2
1),(
,,0 LQV T
t
t
uuxtt dXQVLXfLXfQVLXf 0 ,00,0 ),(),(),(
),( ,0 tt QVLXf ),( 0 LXf
),( 0 LXf LQV ,0:
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Bruno Dupire 34
Daily P&L Variation
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Bruno Dupire 35
Tracking Error Comparison
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Bruno Dupire 36
Hedge with Variance Call
• Start from and delta hedge f in “business time”• If V < L, you have been able to conduct the replication
until T and your wealth is
• If V > L, you “run out of quadratic variation” at < T. If you then replicate f with 0 vol until T, extra cost:
where• After appropriate delta hedge,dominates which has a market price
)(22
)(''2
1LV
MdQV
MdQVXf fT
tfT
tt
VL
f CallM
LXf2
),( 0
)( TXf ),( 0fLXf
)}(''sup{ xfM f
),( 0 LXf
)(),( TT XfVLXf
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Bruno Dupire 37
Super-replication
),( 0 LXf )( TXf
LVT )1
0LV
LVT )2
hedgeBT )( Xf )(2
LVM f
T
),( 0 LXf
0
hedgeBT )(),( TTT XfVLXf
T
VL
ff CM
LXfLXf2
),(),( 00
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Bruno Dupire 38
Lower Bound for Variance Call
• : price of a variance call of strike L. For all f,
• We maximize the RHS for, say,
• We decompose f as
Where if and otherwise
Then,
Where is the price of for variance v and is the market implied variance for strike K
)),(),((2
00 LXfLXfM
C f
f
VL
dKxVanillaKfXfXxXfxf K )()('')(')()()( 000
xKxVanilla K )(
VLC
0XK Kx dKLVanLVanKfC K
KK
VL ))()()((''
)( KK LVan )(xVanilla K
KL
2fM
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Bruno Dupire 39
Lower Bound Strategy• Maximum when f” = 2 on , 0 elsewhere
• Then, (truncated parabola)
and dKLVanLVanC KA
KK
VL ))()((2
}:{ LLKA K
dKxVanillaxfA K )(2)(
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Bruno Dupire 40
Arbitrage Summary
• If a Variance Call of strike L and maturity T is below its lower bound:
• 1) at t = 0,– Buy the variance call
– Sell all options with implied vol
• 2) between 0 and T,– Delta hedge the options in business time
– If , then carry on the hedge with 0 vol
• 3) at T, sure gain
T
L
T
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Bruno Dupire 41
IV Conclusion
• Skew denotes a correlation between price and vol, which links options on prices and on vol
• Business time delta hedge links P&L to quadratic variation
• We obtain a lower bound which can be seen as the real intrinsic value of the option
• Uncertainty on V comes from a spot correlated component (IV) and an uncorrelated one (TV)
• It is important to use a model calibrated to the whole smile, to get IV right and to hedge it properly to lock it in