Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers...

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Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009

Transcript of Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers...

Page 1: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Functional Itô Calculus and

PDEs for Path-Dependent Options Bruno Dupire

Bloomberg L.P.

Rutgers University Conference

New Brunswick, December 4, 2009

Page 2: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Outline1) Functional Itô Calculus

• Functional Itô formula• Functional Feynman-Kac• PDE for path dependent options

2) Volatility Hedge

• Local Volatility Model• Volatility expansion• Vega decomposition• Robust hedge with Vanillas• Examples

Page 3: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

1) Functional Itô Calculus

Page 4: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Why?

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path,current theof functions it to extend We

processes. of functions with deals calculus Itô

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Page 5: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Review of Itô Calculus

• 1D

• nD

• infiniteD

• Malliavin Calculus

• Functional Itô Calculus

current value

possible evolutions

Page 6: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Functionals of running paths

)( and )( is at of valueThe

)(,for ,: :functional

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Page 7: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Examples of Functionals

hit is last value First time-

average rolling ofMax -

variablesstate ofnumber Infinite

(3) rangeon Option -

(2)Asian -

(1)European -

: time)(excluding variablesstate ofnumber Finite

priceoption dependent path of caseimportant thecovers onelast The

price greplicatin-Super -

valuefinal ofn expectatio lConditiona-

drawdownCurrent -

averageCurrent -

Page 8: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Derivatives

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0

0

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],0[

Page 9: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Examples

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Page 10: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Topology and Continuity

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Page 11: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Functional Itô Formula

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Tf

Xx

tCxC

smoothf

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1

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Theorem

.continuous- s themselvesderivative e with thes,in and in

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Page 12: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Fragment of proof

))()((

))()((

))()((

)()(

ff

ff

ff

ffdf

Page 13: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Functional Feynman-Kac Formula

))( martingale the toformula Itô functional(apply

0)(2

)()()()()()(

satisfiesit smooth, is if Then,

)()(],,[for

)()(],,0[for where

]|)([)(

by : define We. :,: ,integrablesuitably For

)()(

payoff.dependent path and dynamics Markovnon FK to oftion Generalisa

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t

u

T

tu

Page 14: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Delta Hedge/Clark-Ocone

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formula Ocone-Clark the tocompared becan It

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)()(]|)([)(

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smooth, is ]|)([)(by defined If

00

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Page 15: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

P&L

St t

St

Break-even points

t

t

Option Value

St

CtCt t

S

Delta hedge

P&L of a delta hedged Vanilla

Page 16: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Functional PDE for Exotics

dependent.path be will and generalin However,

. variablesstate ofnumber infinitean even with options,dependent

pathfor holds also optionsEuropean for off- trade The

0))()(()()(2

1)(

satisfiesit then

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tu

Page 17: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Classical PDE for Asian

term.convection bothering a is

02

10

2

1

,0

),,()(, Define ),( Assume

)()( :CallAsian of Payoff

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Page 18: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Better Asian PDE

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10

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1

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)()(

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),,()(,)(][ Define

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Page 19: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

2) Robust Volatility Hedge

Page 20: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Local Volatility Model• Simplest model to fit a full surface• Forward volatilities that can be locked

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CK

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Page 21: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Summary of LVM

• Simplest model that fits vanillas

• In Europe, second most used model (after Black-Scholes) in Equity Derivatives

• Local volatilities: fwd vols that can be locked by a vanilla PF

• Stoch vol model calibrated

• If no jumps, deterministic implied vols => LVM

),(][ 22 tSSSE loctt

Page 22: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

S&P500 implied and local vols

Page 23: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

S&P 500 FitCumulative variance as a function of strike. One curve per maturity.Dotted line: Heston, Red line: Heston + residuals, bubbles: market

RMS in bpsBS: 305Heston: 47H+residuals: 7

Page 24: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Hedge within/outside LVM

• 1 Brownian driver => complete model

• Within the model, perfect replication by Delta hedge

• Hedge outside of (or against) the model: hedge against volatility perturbations

• Leads to a decomposition of Vega across strikes and maturities

Page 25: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Implied and Local Volatility Bumps

implied to

local volatility

Page 26: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

P&L from Delta hedging

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Page 27: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Model Impact

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Page 28: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Comparing calibrated models

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Page 29: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Volatility Expansion in LVM

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1),( where

),(),()(

])([),(),(2

1)()(

),(),( : form theof LVM a is wherecase In the

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Page 30: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Fréchet Derivative in LVM

)derivative(Fréchet ),(at variancelocal the to ofy sensitivit theis

])([),(2

1),(

where

),(),(

])([),(),(2

1

)()(lim,

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)()(

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Page 31: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

One Touch Option - Price

Black-Scholes model S0=100, H=110, σ=0.25, T=0.25

Page 32: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

One Touch Option - Γ

Page 33: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

PtSmTO ..2

1),(:..

Page 34: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Up-Out Call - Price

Black-Scholes model S0=100, H=110, K=90, σ=0.25, T=0.25

Page 35: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Up-Out Call - Γ

Page 36: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

PtSmUOC ..2

1),(:

Page 37: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Black-Scholes/LVM comparison

price. LVM reach the toenables Scholes-Black theofinput y volatilitno case, In this

Page 38: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Vanilla hedging portfolio I

?),(function get the can we How

),(])([])([),(

t)(x, allfor ifonly and if moves volatility

small all hedges vanillasof ),( Portfolio

.at variancelocal theto

ofy sensitivit theis ])([),(2

1),( Recall

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Page 39: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Vanilla hedging portfolios II

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bumps vollocal y tosensitivit no has and 0]),()([,,

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1)( take weIf c)

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Page 40: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Example : Asian option

.at variancelocal theto ofy sensitivit theis ])([),(2

1),(

1maturity20 volatility100S, )( : off-Pay v

g

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00 (x,t) xxXfEtxtxm

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ttxx

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tttt

v

K

T

KT

Page 41: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Asian Option Hedge

KT

20

2

,

),(),(

2

1),(),(

),( with hedgeatility Robust vol

x

TKhTKv

t

TKhTK

dTdKCTKPF TK

K

T

Page 42: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Fwd Start Option Hedge

dTdKCTKPF

xxXg

TK

TTT

,),( with hedgeatility Robust vol

)()(122

Page 43: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Γ/VegaLink

Page 44: Functional Itô Calculus and PDEs for Path-Dependent Options Bruno Dupire Bloomberg L.P. Rutgers University Conference New Brunswick, December 4, 2009.

Conclusion

• Itô calculus can be extended to functionals of price paths

• Price difference between 2 models can be computed

• We get a variational calculus on volatility surfaces

• It leads to a strike/maturity decomposition of the volatility risk of the full portfolio