Stochastic Processes for Finance

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A lecture note on stochastic processes for finance by Patrick Roger (bookboon)

Transcript of Stochastic Processes for Finance

Patrick Roger

Stochastic Processes forFinance

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Stochastic Processes for Finance

Patrick Roger

Strasbourg University, EM Strasbourg Business School

June 2010

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Stochastic Processes for Finance© 2010 Patrick Roger & Ventus Publishing ApSISBN 978-87-7681-666-7

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Stochastic Processes for Finance

4

Contents

Contents Introduction 7

1 Discrete-time stochastic processes 91.1 Introduction 91.2 The general framework 101.3 Information revelation over time 121.3.1 Filtration on a probability space 121.3.2 Adapted and predictable processes 141.4 Markov chains 171.4.1 Introduction 171.4.2 Definition and transition probabilities 191.4.3 Chapman-Kolmogorov equations 191.4.4 Classification of states 211.4.5 Stationary distribution of a Markov chain 241.5 Martingales 251.5.1 Doob decomposition of an adapted process 291.5.2 Martingales and self-financing strategies 301.5.3 Investment strategies and stopping times 341.5.4 Stopping times and American options 39

2 Continuous-time stochastic processes 432.1 Introduction 43

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Stochastic Processes for Finance

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Contents

2.2 General framework 442.2.1 Filtrations, adapted and predictable processes 482.2.2 Markov and diffusion processes 512.2.3 Martingales 532.3 The Brownian motion 552.3.1 Intuitive presentation 552.3.2 The assumptions 572.3.3 Definition and general properties 612.3.4 Usual transformations of the Wiener process 642.3.5 The general Wiener process 672.3.6 Stopping times 692.3.7 Properties of the Brownian motion paths 71

3 Stochastic integral and Itô’s lemma 733.1 Introduction 733.2 The stochastic integral 753.2.1 An intuitive approach 753.2.2 Counter-example 783.2.3 Definition and properties of the stochastic integral 803.2.4 Calculation rules 833.3 Itô’s lemma 853.3.1 Taylor’s formula, an intuitive approach to Itô’s lemma 863.3.2 Itô’s lemma 883.3.3 Applications 883.4 The Girsanov theorem 91

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Stochastic Processes for Finance

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Contents

3.4.1 Preliminaries 913.4.2 Girsanov theorem 933.4.3 Application 933.5 Stochastic differential equations 953.5.1 Existence and unicity of solutions 953.5.2 A specific case: linear equations 97

Bibliography 100

Index 103

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Stochastic Processes for Finance

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Introduction

0, 1, .., T. [0;T ] .

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Stochastic Processes for Finance

8

Introduction

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Stochastic Processes for Finance

9

Discrete-time stochastic processes

T

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Stochastic Processes for Finance

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Discrete-time stochastic processes

T = {0, 1, ..., T} T < +∞. T− = {0, 1, ..., T − 1} ; T − 1 T. T

(,A, P ) A P A.

X = (X0, ...,XT ) (,A) R.

Xt n

R BR

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Discrete-time stochastic processes

Xt X

Xt = Xt−1 × Yt

Yt u d p 1 − p. X0 X Xt Xt R+

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�e Graduate Programme for Engineers and Geoscientists

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supervisor in the North Sea

advising and helping foremen

solve problems

I was a

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Real work International opportunities

�ree work placementsal Internationaor�ree wo

I wanted real responsibili� I joined MITAS because

Maersk.com/Mitas

�e Graduate Programme for Engineers and Geoscientists

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supervisor in the North Sea

advising and helping foremen

solve problems

I was a

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Real work International opportunities

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I wanted real responsibili� I joined MITAS because

Maersk.com/Mitas

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I wanted real responsibili� I joined MITAS because

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Discrete-time stochastic processes

X1(ω) X2(ω)ω1 ω2 ω3 ω4

X1 X2

Xt X1

X2 X1 X2 .

= {ωi, i = 1, ..., 4} A = P() X1 X2

X1 X1 =100 {ω1, ω2} X1 =105 {ω3, ω4} . X2 X1 {ω1, ω2} X1 T = 2 X2

X1 X2

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Discrete-time stochastic processes

X1 X2 σ X1

σ (X1, X2) .

t t

s t

(,A, P ) F = {F0,F1, ...,FT} A. (,A, P,F)

Ft

Ft−1 ⊆ Ft

t ≥ 1. T,

T. FT =A.

F0 = {∅ }

F0 = {∅,}F1 = {∅, {ω1, ω2} , {ω3, ω4} ,}F2 = P()

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Discrete-time stochastic processes

t = 1 {ω1, ω2} {ω3, ω4} X1

X Xt

t. t, Xt Xt t+ 1 Xt+1 Xt t t.

F = {F0,F1, ...,FT} Ft t {Xt ≤ 100} Xt Xt Ft t).

X = (X0, ..., XT ) F t, Xt Ft

X FX A FX

t Xs, s ≤ t.

Xt =Xt−1Yt Yt u d p 1− p. T = 2, X t = 0 T = 2. 4 = {uu, ud, du, dd} X1 up down . {uu, ud} {du, dd}

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Discrete-time stochastic processes

F1 = {∅, {uu, ud} , {du, dd} ,} . X1

X1(uu) = X1(ud) = uX0

X1(du) = X1(dd) = dX0

F1 F = FX .

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Stochastic Processes for Finance

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Discrete-time stochastic processes

X0 X1(ω) X2(ω)ω1 1 u u2

ω2 1 u udω3 1 d duω4 1 d d2

X

Card() = n n L2 (,A, P ) Rn. L2 (,Ft, P ) , Ft L2 (,A, P )

t. L2 (,F1, P )

L2 (,F1, P ) =(x, y, z, t) ∈ R4 x = y z = t

F1 X0 = 1

K Xk, k = 1, ...,K.

t t+1. t t + 1 θ′

t =θ1t , ...θ

Kt

θ′

t+1 =θ1t+1, ...θ

Kt+1

t t+ 1.

θt+1 t

X = (X1..., XT ) t ≥ 1, Xt Ft−1

t = 1

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Stochastic Processes for Finance

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Discrete-time stochastic processes

X1 F0 θ θ0 θ0 θ1.

rt [t; t+ 1] Bt t

rt t r F , B

Bt =t−1

s=0

(1 + rs)

Bt t− 1 Bt Ft−1

p,

p+1. p p+1.

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Stochastic Processes for Finance

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Discrete-time stochastic processes

p p+1.

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Stochastic Processes for Finance

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Discrete-time stochastic processes

X (,A, P,F) (B1, ..., Bn) ∈ Bn

R (t1, ..., tn) ∈ T n t1 < .... < tn

PXt ∈ Bn

Xt ∈ Bj, j = 1, .., n− 1= P

Xt ∈ Bn

Xt− ∈ Bn−1

tn−1

tn−1 tn. tn−1.

(Xp, p ∈ N) (x1, ..., xn)

(Xp, p ∈ N) (x1, ..., xn) ;

π(xi, p− 1, p, xj) = P (Xp = xj |Xp−1 = xi )

Πp−1 = (π(xi, p− 1, p, xj), i, j = 1, ..., n) p− 1.

p, π(xi, p− 1, p, xj) = πij

π(xi, p − 1, p, xj) X xj

p xi p − 1.

m

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Discrete-time stochastic processes

m

X = (Xp, p ∈ N) N π = (πij, i, j = 1, ...,N) .

π(m+n)ij = P (Xm+n = xj |X0 = xi ) m + n

i j.

π(m+n)ij =

n

k=1π

(m)ik π

(n)kj

m = n = 1 N = 3. π(2)12

x2 x1.

πր x1

πց

x1π→ x2

π→ x2

ցπ x3

րπ

π(2)12 = π11π12 + π12π22 + π13π32

xi xj m + n π

(m+n)ij i m

π

(m)ij , i, j = 1, ..., n

j n

π

(n)ij , i, j = 1, ..., n

.

X = (Xp, p ∈ N) N π = (πij, i, j = 1, ...,N) .

π(m)ij = P (Xm = xj |X0 = xi ) m

i j π(m) m π

(m)ij .

π(m) = πm

πm m π.

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Stochastic Processes for Finance

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Discrete-time stochastic processes

xj X xi k > 0

π(k)ij > 0

xi xj k k′

π(k)ij > 0 π

(k′)ji > 0

π.

π =

0.6 0.4 0 00.3 0.7 0 00 0 0.5 0.50 0 0.7 0.3

x1 x2, x3

x4 x1

x2 x3 x4 x1 x2 {x1, x2} {x3, x4} .

X = (Xp, p ∈ N) N π = (πij , i, j = 1, ..., N) . (C1, C2, ...CM) N Ck

R, x R x x x

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Stochastic Processes for Finance

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Discrete-time stochastic processes

S t+ 1

St+1 =

uSt pdSt 1− p

d = 1/u. S0

X = (Xp, p ∈ N) N π = (πij , i, j = 1, ..., N) .

xi, t(i) m π(m)(i, i) > 0. t(i) = 0 m, π(m)(i, i) = 0.

X t(i) = 1 i.

X = (Xp, p ∈ N) π.

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Stochastic Processes for Finance

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Discrete-time stochastic processes

pi(n) i n i +∞

i=1pi(n) = 1

i

limm→+∞

π(m)ii = qi > 0

i i n

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Discrete-time stochastic processes

X = (Xp, p ∈ N) .

qi =+∞

j=1qjπij

+∞

i=1qi = 1. qi

∀i, qi ≥ 0 +∞

i=1qi = 1

qi =+∞

i=1qjπij

qi

πm m πm (qi, i = 1, ..., n).

π =

0.6 0.40.4 0.6

π

π2 =

0.52 0.480.48 0.52

π3 =

0.504 0.4960.496 0.504

lim

m→+∞πm =

0.5 0.50.5 0.5

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Stochastic Processes for Finance

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Discrete-time stochastic processes

(Y,Z) L2 (,A, P ) B,B′

A B ⊂ B′

Z c ∈ R, E (Z |B ) = c

∀(a, b) ∈ R2, E (aZ + bY |B ) = aE (Z |B ) + bE (Y |B )

Z ≤ Y, E (Z |B ) ≤ E (Y |B )

E (E (Z |B′ ) |B ) = E (Z |B )

Z B E (ZY |B ) = Z E (Y |B )

Z B, E (Z |B ) = E(Z)

(,A,F , P ) X = (X0, ..., XT ) (F , P )

X F ∀t ∈ T , Xt ∈ L1 (,A, P ) ∀t ∈ T ∗, Xt−1 = E [Xt |Ft−1 ]X (F , P ) Xt−1 ≥ E [Xt |Ft−1 ]X (F , P ) Xt−1 ≤ E [Xt |Ft−1 ]

(F , P )

P

E [Xt −Xt−1 |Ft−1 ] = 0 X F Xt−1 Ft−1

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Discrete-time stochastic processes

X (s, t), s ≤ t

E [Xt |Fs ] = (≤,≥) Xs

E [Xt+1 |Ft−1 ] . Xt−1 =E [Xt |Ft−1 ] Xt = E [Xt+1 |Ft ] .

Xt−1 = E [E [Xt+1 |Ft ] |Ft−1 ]

Xt−1 = E [Xt+1 |Ft−1 ]

Ft−1 ⊂ Ft+1.

X E [Xt |Ft−1 ] Ft−1 Xt X Xt−1 Xt t− 1. Xt−1 Xt Ft−1

E(Xt) .

X X0 = c ∈R

Xt = Xt−1 + Yt

Yt

E (E [X |F− ]) = E [X] = E [X−]

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Discrete-time stochastic processes

X0 t Xt

Xt = Xt−1 + Yt

Yt t Ys

t

Xt = X0 +t

s=1

Ys

F Y, Fs Yu, u ≤ s. Ys s Xs s X F

E [Xt |Ft−1 ] = E [Xt−1 + Yt |Ft−1 ]

= E [Xt−1 |Ft−1 ] + E [Yt |Ft−1 ]

X F, Xt−1 Yt Yt Ft−1. E [Yt |Ft−1 ] = E [Yt] = 0⇒ E [Xt |Ft−1 ] = Xt−1, X .

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Discrete-time stochastic processes

Y ∈ L1 (,A, P ) X

Xt = E [Y |Ft ]

X

E [Xt |Ft−1 ] = E [E [Y |Ft ] |Ft−1 ]

= E [Y |Ft−1 ] = Xt−1

E [Xt] t)

T t ≤ T Y ( Z) Y Z).

Xt = Yt − Zt t X0 = 0

Xt = Xt−1 + δt

δt = 1 t δt = −1

E [Xt |Ft−1 ] = Xt−1 + E [δt |Ft−1 ]

δt Ft−1. Yt = s Zt = t−s

P (δt+1 = 1 |Yt = s) =T2− s

T − t

12 s = t

2,

E [δt+1 |Yt = s ] = 0.

X t, E(Yt) = E(Zt) =t2 E [Xt] = 0. X

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Discrete-time stochastic processes

0 t t

2

Y Z YT = ZT = T

2 XT = 0.

F0

X F , Xt X

∀t ∈ T , Xt = X0 +Mt +At

M M0 = 0 A A0 = 0. X A (At ≤ At+1)

Xt = X0 +Mt +At,

E [Xt −Xt−1 |Ft−1 ] = E [Mt −Mt−1 |Ft−1 ] + E [At − At−1 |Ft−1 ]

M A At − At−1.

At =t

s=1

E [Xs −Xs−1 |Fs−1 ]

X A M

Mt = Xt −X0 −t

s=1

E [Xs −Xs−1 |Fs−1 ]

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Discrete-time stochastic processes

Xt 0 t [s; s+ 1]

Xt −Xt−1 = Mt −Mt−1 +At − At−1

E [Xt −Xt−1 |Ft−1 ] = E [At − At−1 |Ft−1 ] = At −At−1

At −At−1

K t Xt = (X1

t , ....,XKt )

Xk θt = (θ1t , ...., θ

Kt )

t− 1 t. t

Vt(θ) =K

k=1

θktXkt

Vt(θ) V0(θ) s, 0 < s < t 5× $100 + 10× $60 = $1100.

5×$130+10×$65 = $1300.

T

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Discrete-time stochastic processes

5 10 100 60 6 8 130 65 4 11 120 80

S

6×$130+8×$65 = $1300,

.

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Discrete-time stochastic processes

θ θ t

K

k=1

θktXkt =

K

k=1

θkt+1Xkt

t θt. θt+1 t

X =X1, ..., XK

RK

θ =θ1, ..., θK

Y

Y0 =K

k=1

θk1Xk0

Yt =K

k=1

θktXkt

Yt t θ, Y0

E [Yt − Yt−1 |Ft−1 ] = E

K

k=1

θktX

kt − θkt−1X

kt−1

|Ft−1

E [Yt − Yt−1 |Ft−1 ] = E

K

k=1

θktXk

t −Xkt−1

|Ft−1

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Discrete-time stochastic processes

K

k=1 θktX

kt =

Kk=1 θ

kt+1X

kt .

θ θt

E [Yt − Yt−1 |Ft−1 ] =K

k=1

θktE

Xkt −Xk

t−1

|Ft−1

X

E [Yt − Yt−1 |Ft−1 ] = 0

E [Yt |Ft−1 ] = Yt−1

X =X1, ..., XK

RK

θ =θ1, ..., θK

Z

Z0 = 0

Zt =K

k=1

t

s=1

θksXk

s −Xks−1

E [Zt − Zt−1 |Ft−1 ] = E

K

k=1

θktXk

t −Xkt−1

|Ft−1

E [Zt − Zt−1 |Ft−1 ] =K

k=1

θktE

Xkt −Xk

t−1

|Ft−1

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Discrete-time stochastic processes

X

Z Y fY FY

E(Y ) =

+∞

−∞y fY (y) dy =

+∞

−∞y dFY (y)

E(Y ) = E(Y ) = Y,L .

Y y1 <y2, ... < yn;

E(Y ) =

yi (FY (yi)− FY (yi−1)) =

+∞

−∞y dFY (y)

Z

Zt =K

k=1

t

0

θksdXks

t

0θksdX

ks θ k

t. E(X) Zt; E(X) Zt Y Xk

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Discrete-time stochastic processes

Xt t;

Xt = X0 +t

s=1

Ys

Ys X0

t Xt = X0 + 1 X0 + 1 T

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Discrete-time stochastic processes

v (,A, P ) T

∀t ∈ T {v = t} = {ω ∈ v(ω) = t} ∈ Ft

ν {v = t} ∈Ft t

FT Ft

, {v = t} {v ≤ t} , {v > t} . Ft−1 ⊂ Ft, {v = t− 1} ∈ Ft−1 {v = t− 1} ∈ Ft. t−k k < t. Ft {v ≤ t} ∈Ft ⇒ {v ≤ t}c = {v > t} ∈ Ft.

v X Xv E(Xv) = E (X0) .

v X K t ∈ T

ω ∈ , |Xt(ω)| ≤ K.

Xt

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Discrete-time stochastic processes

t , ν

Y

Yt =

Xt t < vXv t ≥ v

Y X, Xv v .

ν X F , Y F X Y.

ξs = {v≥s}; {v ≥ s} Y

Yt = X0 +t

s=1

ξs (Xs −Xs−1)

t < v, Yt = Xt. t > v 1 s ≤ v Yt = Xv. {v ≥ t} = {v ≤ t− 1}c ξ Y

X Y ξ

E [Yt − Yt−1 |Ft ] = E [ξt (Xt −Xt−1) |Ft ]

= ξtE [(Xt −Xt−1) |Ft ]

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Discrete-time stochastic processes

X

E [(Xt −Xt−1) |Ft ] = 0

X0 = 0

t

Xt = −t

s=1

2s−1 = −t−1

s=0

2s

t

Xt = −2t − 1

2− 1= −(2t − 1)

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Stochastic Processes for Finance

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Discrete-time stochastic processes

t+ 1 2t. Xt+1 = 1 = X0 + 1.

v = inf {t / Xt = 1}

E(v) =+∞

s=1

1

2s

s

+∞

s=1

1

2s

s =

+∞

s=1

+∞

u=s

1

2u

+∞

u=s

12

= 1

2−

E(v) =+∞

s=1

1

2s= 2

v X

T K K, T T ).

Yt t. Yt = max(K − St; 0), St t t K ≤ St. K

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Discrete-time stochastic processes

K − St

Y F X

XT = YT

Xt = max (Yt;E (Xt+1 |Ft )) t < T

Y.

X Y Y, Xt ≥ Yt t.

Xt ≥ E (Xt+1 |Ft ) ; X Z Y ∀t, Zt ≥ Xt . t = T X. Zs ≥ Xs s ≥ t0 Zt−1 ≥ Xt−1.

Z

Zt−1 ≥ E (Zt |Ft−1 ) ≥ E (Xt |Ft−1 )

Z Y ;

Zt−1 ≥ Yt−1

Zt−1 ≥ max (Yt−1;E (Xt |Ft−1 )) = Xt−1

t.

t Xt = Yt.

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Discrete-time stochastic processes

v

v = inf (t / Xt = Yt)

v Xv

X v ≤ T

{v = s} =s−1

u=1

{Xu > Yu}{Xs = Ys}

Fs Xu, Yu, u ≤ s Fs {v = s} ∈ Fs v

Xvt ξs = {v≥s},

Xvt = X0 +

t

s=1

ξs (Xs −Xs−1)

Xs = max (Ys;E (Xs+1 |Fs )) Xvs (ω) = Xs(ω) ω ∈

{v ≥ s} ,

Xs(ω) = E (Xs+1 |Fs ) (ω) ω ∈ {v ≥ s} Xv

s − Xvs−1 = ξs (Xs −E (Xs |Fs−1 )) .

{v ≥ s} , Xs−Xs−1 Xs. {v < s} , Xv

s = Xvs−1 = Yv

{v < s}

EXv

s −Xvs−1 |Fs−1

= E (ξs (Xs −E (Xs |Fs−1 )) |Fs−1 )

ξ ξs

EXv

s −Xvs−1 |Fs−1

= 0

Xv

v

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Stochastic Processes for Finance

42

Discrete-time stochastic processes

T T, v

v = inf {t / Xt = Yt}

E [Yv] = supu∈T

E [Yu]

Yt = max(K−St; 0) t Xt − Yt

T T, v

v = inf {t / Xt = Yt}

E [Yv] = supu∈T

E [Yu]

Yt = max(K−St; 0) t Xt − Yt

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Stochastic Processes for Finance

43

Continuous-time stochastic processes

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Stochastic Processes for Finance

44

Continuous-time stochastic processes

(,A, P ) T T = [0;T ] , T < +∞

X =(Xt, t ∈ T ) (R,BR) .

T

t → Xt(ω) ω ∈ X.

[0;T ]

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Stochastic Processes for Finance

45

Continuous-time stochastic processes

t→ Xt(ω)

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Stochastic Processes for Finance

46

Continuous-time stochastic processes

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Stochastic Processes for Finance

47

Continuous-time stochastic processes

• Xt(ω) Xs(ω), s < t

T .

Y X t ∈ T t = {Xt = Yt} Y X.

X Y

∃∗ ∈ A P (∗) = 1 ∀ω ∈ ∗, ∀t ∈ T , Xt(ω) = Yt(ω)

= [0; 1] , T =[0; 1] X Xt = 0 t ω. Y

Yt(ω) = 1 t = ω

= 0

P ({Xt = Yt}) = 1 P Y X. X Y

t t = ∅.

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Stochastic Processes for Finance

48

Continuous-time stochastic processes

F = {Ft, t ∈ T } A. (,A,F , P )

F t < T, Ft =

s>tFs

F Ft

X F t, Xt Ft

X, FX , X FX

t Xs, s ≤ t.

P (ω) > 0 ω. B ∈ Ft P (B) = 0 A B Ft. P (A) ≤ P (B) A ⊂ B P (A) A

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Stochastic Processes for Finance

49

Continuous-time stochastic processes

t t − 1 Xt Ft−1 Xt Ft−1

X F

f : R→R f(x) x < x0. f(x0)?

f

limx→xx<x

f(x) = f(x0)

x → x0 x < x0 x →x−

0 .

t < t0, t

X (,A, P ) ;

X t1 ≤ t2 ≤ ... ≤ tn, Xt −Xt−

X t ∈ T h > 0 t + h ∈ T , Xt+h −Xt h t

B∗ ×T B∗

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Stochastic Processes for Finance

50

Continuous-time stochastic processes

X

X0 = 0

Xt = Xt−1 + Yt

Yt X Yt.

Xt S) Xt − Xs [s; t] . Xt = ln(St)

Xt −Xs = ln(St)− ln(Ss) = ln

St

Ss

X Xt −Xs

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Stochastic Processes for Finance

51

Continuous-time stochastic processes

X λ X0 = 0 X (s, t) ∈ R+×R+ s ≤ t, Xt−Xs

λ(t− s).

∀k ∈ N, P (Xt −Xs = k) =(λ(t− s))k

k!exp (− (λ(t− s)))

Xt N .

Y t,

Claimt =X

i=1

Yi

Xt t.

h h.

P (Xt+h −Xt = 2) =(λh)2

2exp (−λh) ≃ λ2

2h2

h h λh.

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Stochastic Processes for Finance

52

Continuous-time stochastic processes

X (,A, P,F) (B1, ..., Bn) ∈ Bn

R (t1, ..., tn) ∈ T n t1 < .... < tn

PXt ∈ Bn

Xt ∈ Bj , j = 1, .., n− 1= P

Xt ∈ Bn

Xt− ∈ Bn−1

Xs s ≤ tn−1 Xt− .

Bn−1 tn−1 Xt −Xt−. tn−1

X X σ R×T R R+

(x, t) = limh→0

E [Xt+h −Xt |Xt = x ]

h

σ2(x, t) = limh→0

V [Xt+h −Xt |Xt = x ]

h

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Stochastic Processes for Finance

53

Continuous-time stochastic processes

σ Xt t Xt+h −Xt [t; t+ h] . (x, t) t X x. h

σ2 σ σ.

(,A,F , P )

(F , P ) X F ,

∀(s, t) ∈ T 2, s ≤ t⇒ E [Xt |Fs ] = Xs

X (F , P ) E [Xt |Fs ] ≤Xs.

X (F , P ) E [Xt |Fs ] ≥Xs.

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Stochastic Processes for Finance

54

Continuous-time stochastic processes

(F , P ) X X X

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Stochastic Processes for Finance

55

Continuous-time stochastic processes

X F X

∀t ∈ T , Xt = X0 +Mt +At

M M0 = 0 A A0 = 0. X A

X

X0 = a

Xn = Xn−1 + Yn

a Yn T [0;T ] N

h = T/N N T

T = {0, 1, ..., N} .

X Yn σ −σ p = 1/2

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Stochastic Processes for Finance

56

Continuous-time stochastic processes

X

Cov(Xn, Xm) = σ2 min(n,m)

Yn. m < n

Cov(Xn, Xm) = Cov(Xm +n

k=m+1

Yk,Xm)

= Cov(Xm,Xm) +n

k=m+1

cov(Yk, Xm)

= V (Xm) +n

k=m+1

Cov (Yk,Xm)

Cov(Xm, Xm) = V (Xm). Cov (Yk, Xm) k > m Yk Xm = X0 +

k≤m Yk

V (Yk) = σ2 V (Xm) =m

k=1 V (Yk) = mσ2. n < m, Cov(Xn,Xm) = V (Xn) = nσ2.

Xn t t = nh X0 = 0 Xn [0; t] . T = {0, 1, ..., N} [0;T ]

N → +∞ [0;T ] ΛT , ΛT = limN→+∞ XN .

σ N Nσ2 [0;T ] T N = 13 N = 91

T

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Stochastic Processes for Finance

57

Continuous-time stochastic processes

σ N u d

Y Nn , n = 1, ...,N

T = {0, 1, ...,N} σh Y Nn

T XN = X0 +Nn=1 Y

Nn .

N, XN −X0 [0;T ] , ΛT . N h

N

A1 > 0, N,

∀n ∈ T , V (Xn) ≥ A1

A2 > 0, N, :

V (XN ) ≤ A2

A3 > 0, N, n ∈ T

V (Yn)

maxNj=1 V (Yj)≥ A3

• 0 t

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Stochastic Processes for Finance

58

Continuous-time stochastic processes

• [0;T ] u d

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Stochastic Processes for Finance

59

Continuous-time stochastic processes

A3 = 1.

O o.

f g R R. f(h) = O(g(h)) limh→0

f(h)g(h)

< +∞ f(h) = o(g(h))

limh→0

f(h)g(h)

= 0.

f(h) = O(g(h)) f g h f(h) = o(g(h)) f g h

σ2h

σ2h = O(h) σ2

h = o(h)

O(h) o(h)

V (XN) =N

n=1

V (Y Nn ) = Nσ2

h

Y Nn .

Nσ2h ≤ A2 N T

h

σ2h ≤

A2

Th

σ2h = O(h).

Nσ2h ≥ A1

σ2h ≥ A

Th. σ2

h = o(h).

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Stochastic Processes for Finance

60

Continuous-time stochastic processes

Yn O(h), Yn σh = c

√h −c

√h c

O(√h) h.

c = 1 X

Xn = Xn−1 + δn√h

δn +1 −1

h = 1

h = 1 h = 0.2

±

√hh, ± 1√

h.

h h h

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Stochastic Processes for Finance

61

Continuous-time stochastic processes

h = 0.2

ΛT = limN→+∞

TN

Nn=1 δn δn

. ΛT

√T .

T T ′ , T < T ′, ΛT ′−ΛT

√T ′ − T ΛT ′ −ΛT

ΛT − Λ0.

,A, P ) Z

Z0 = 0 P.a.s

Z

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Stochastic Processes for Finance

62

Continuous-time stochastic processes

∀(s, t) ∈ R+ ×R+, s < t, Zt − Zs ∼ N (0,√t− s)

Z

Zt

√t.

Z t t + h

√h

h

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Stochastic Processes for Finance

63

Continuous-time stochastic processes

Z

Z

cov(Zt, Zs) = min(t, s)

(1)

Zt =Zs + (Zt − Zs) s < t) Zs (Zt − Zs) (Zt − Zs) FZ

s , Z.

(2), s t s < t.

EZt

FZs

= E

Zs + (Zt − Zs)

FZs

= EZs

FZs

+ E

Zt − Zs

FZs

Zt−Zs FZ

s EZt − Zs

FZs

= E [Zt − Zs] = 0

Z FZ , Zs FZs

EZs

FZs

= Zs

EZt

FZs

= Zs

(3), s < t)

cov(Zt, Zs) = cov(Zt − Zs + Zs, Zs)

= cov(Zt − Zs, Zs) + V (Zs)

= cov(Zt − Zs, Zs − Z0) + s

= s

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Stochastic Processes for Finance

64

Continuous-time stochastic processes

Zt − Zs Zs − Z0

t < s, Zs Zs − Zt + Zt .

(x, t) = limh→0

E [Xt+h −Xt |Xt = x ]

h

σ2(x, t) = limh→0

V [Xt+h −Xt |Xt = x ]

h

((x, t) = 0) σ(x, t) 1

X Y

Xt = Z2t − t

Yt = exp

γZt −

γ2t

2

γ X Y FZ .

Z Xt Zt Yt

Zt = Zs+(Zt−Zs) s < t. E

Xt

FZs

EXt

FZs

= E

Z2

t

FZs

− t

= E(Zt − Zs)

2 + 2ZtZs − Z2s

FZs

− t

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Stochastic Processes for Finance

65

Continuous-time stochastic processes

Zs FZ

s − Z Zt − Zs FZ

s .

EXt

FZs

= E

(Zt − Zs)

2FZ

s

+ 2E

ZtZs

FZs

− E

Z2

s

FZs

− t

= E(Zt − Zs)

2FZ

s

+ 2E

ZtZs

FZs

− Z2

s − t

= E(Zt − Zs)

2FZ

s

+ 2ZsE

Zt

FZs

− Z2

s − t

= E(Zt − Zs)

2FZ

s

+ 2Z2

s − Z2s − t

E(Zt − Zs)

2+ Z2

s − t

(t− s) + Z2s − t

= Z2s − s = Xs

Y

EYt

FZs

= exp

−γ2t

2

Eexp (γZt)

FZs

= exp

−γ2t

2

Eexp (γ(Zt − Zs)) exp(γZs)

FZs

= exp

−γ2t

2

E (exp (γ(Zt − Zs))) exp(γZs)

exp (γ(Zt − Zs)) 0 γ

√t− s. E (exp (γ(Zt − Zs))) = γ(t−s)

2.

EYt

FZs

= exp

γZs −

γ2s

2

= Ys

X Y

(x, t) → f(x, t) = x2 − t

(x, t) → g(x, t) = exp

γx− γ2t

2

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Stochastic Processes for Finance

66

Continuous-time stochastic processes

z(x, t) Zt

z(x, t) =1√2πt

exp

−x2

2t

f g

h(0, s) =

+∞

−∞h(u, t+ s)z(u, t)du

f, f(0, s) = −s

+∞

−∞f(u, t+ s)z(u, t)du =

1√2πt

+∞

−∞(u2 − (t+ s)) exp

−u2

2t

du

=1√2πt

+∞

−∞u2 exp

−u2

2t

du− (t+ s)

1√2πt

+∞−∞ u2 exp

−u

2t

du Zt, t. g,

+∞

−∞g(u, t+ s)z(u, t)du =

1√2πt

+∞

−∞exp

γu− γ2(t+ s)

2− u2

2t

du

= exp

−γ2(t+ s)

2

1√2πt

+∞

−∞exp

γu− u2

2t

du

exp

γu− u2

2t

= exp

− 1

2t(u− γt)2 +

γ2t

2

+∞

−∞g(u, t+ s)z(u, t)du = exp

−γ2s

2

1√2πt

+∞

−∞exp

− 1

2t(u− γt)2

du

= exp

−γ2s

2

= g(0, s)

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Stochastic Processes for Finance

67

Continuous-time stochastic processes

E(Zt) = 0 t. V (Zt) = t

σ σ2

W σ Wt

W0 = 0

Wt = t+ σZt

Z

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Stochastic Processes for Finance

68

Continuous-time stochastic processes

W Z.

W

s < t, Wt −Ws ∼ N ((t− s), σ

t− s)

cov(Wt,Ws) = σ2(t− s)

W (<)0.

W

1. Wt [0; t] , 0 r, r.

h W [t; t+ h]

Wt+h −Wt = h+ σ(Zt+h − Zt)

Z, Zt+h−Zt

O(√h), h h

W , σ(Zt+h − Zt) h h .

= 0, [a; b])

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Stochastic Processes for Finance

69

Continuous-time stochastic processes

=10% σ = 20%)

= 10% σ = 20%, . t = 0.75),

(,A,F , P ) τ T {+∞} {τ ≤ t} ∈Ft t ∈ T .

{τ ≤ t} {τ = t} . P ({τ = t}) = 0 t

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Stochastic Processes for Finance

70

Continuous-time stochastic processes

+∞ τ = +∞.

T (C1, ..., CT ) t Ft < Ct τ τ = inf {t / Ft < Ct} τ = +∞ T.

τ τ ′ F τ + τ ′, min(τ , τ ′), max(τ , τ ′)

min(τ , τ ′) τ ∧ τ ′ τ ∨ τ ′ max(τ , τ ′).

τ ∧ τ ′ τ ∨ τ ′ τ τ ′ min(τ , τ ′)) max(τ , τ ′)) |τ − τ ′|

t t ∧ τ

Fτ τ A {τ ≤ t} A ∈ A

τ X

τ Fτ

τ X F Xτ Fτ

τ τ ′ τ ≤ τ ′, Fτ ⊆ Fτ ′

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Stochastic Processes for Finance

71

Continuous-time stochastic processes

W (0, σ) a b a < 0 < b τ

τ = inft ∈ R+ / Wt = a Wt = b

P (Wτ = a) =b

b− a

E(τ) =−ab

σ2

τ a b. a τ b σ, τ a b σ P (Wτ = a) =P (Zτ = a).

X,

Xt = Z2t − t

τ ∗ = τ ∧ s s Xτ∗

t EXτ∗

t

= E(Xτ∗

0 ) = 0

EZ2

t∧τ∗

= E(t ∧ τ ∗) ≤ (b− a)2

τ Z a b. s,

E(τ ) = lims→+∞

E(τ ∗) ≤ (b− a)2

τ E(Zτ ) = E(Z0) = 0

E(Zτ ) = aP (Zτ = a) + b(1− P (Zτ = a)) = 0

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Stochastic Processes for Finance

72

Continuous-time stochastic processes

P (Zτ = a) =b

b− a

X

E (Xτt ) = 0 = E

Z2

τ − τ

E(Z2τ ) = a2 b

b− a+ b2

−a

b− a= −ab

Wt = σZt,

Z

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Stochastic Processes for Finance

73

Stochastic integral and Itô’s lemma

Yn.

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Stochastic Processes for Finance

74

Stochastic integral and Itô’s lemma

(x, t) σ(x, t)

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Stochastic Processes for Finance

75

Stochastic integral and Itô’s lemma

[0;T ] N h h = T/N. 0, h, 2h, ...., Nh

X

X0 = 0

Xn = Xn−1 + Yn

n ∈ T = {1, ...,N} . Xn(ω) X nh ω h Yn h.

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Stochastic Processes for Finance

76

Stochastic integral and Itô’s lemma

Yn

Yn = n−1h+ σn−1

Znh − Z(n−1)h

Z n−1 σn−1 F(n−1)h F Z n−1 σn−1

n−1 X [(n− 1)h;nh] σn−1 σ− F(n−1)h. σn−1 Znh−Z(n−1)h V

Znh − Z(n−1)h

= h.

X Y

ΛT [0;T ] ΛT

XN N

XN =N

n=1

Yn = hN

n=1

n−1 +N

n=1

σn−1

Znh − Z(n−1)h

h TN,

XN =N

n=1

Yn = T

1

N

N

n=1

n−1

+N

n=1

σn−1

Znh − Z(n−1)h

= AN +BN

AN = T

1N

Nn=1 n−1

BN =

Nn=1 σn−1

Znh − Z(n−1)h

A = limN→+∞ AN B = limN→+∞ BN .

αN

αNs = n−1 s ∈ [(n− 1)h;nh[

αNs (ω)

[(n− 1)h;nh[ . αN(ω)

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Stochastic Processes for Finance

77

Stochastic integral and Itô’s lemma

ω,

AN (ω) =

T

0

αNs (ω)ds

N → +∞ ω, A(ω)

A(ω) =

T

0

αs(ω)ds

αs(ω) = limN→+∞ αNs (ω). α

BN ,

Znh − Z(n−1)h

√h.

σ Z

limN→+∞

BN =

T

0

σsdZs

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Stochastic Processes for Finance

78

Stochastic integral and Itô’s lemma

CN DN

CN =N

n=1

Znh

Znh − Z(n−1)h

DN =N

n=1

Z(n−1)h

Znh − Z(n−1)h

Z Z

limN→+∞

CN = limN→+∞

DN =

T

0

ZsdZs =Z2

T − Z20

2=

Z2T

2

CN DN N Zt = Zs + (Zt−Zs)

E(CN ) = E

N

n=1

Znh

Znh − Z(n−1)h

=N

n=1

EZnh

Znh − Z(n−1)h

Znh = Z(n−1)h + (Znh − Z(n−1)h),

E(CN) =N

n=1

E

Z(n−1)h + (Znh − Z(n−1)h)

Znh − Z(n−1)h

= E

N

n=1

Z(n−1)h

Znh − Z(n−1)h

+E

N

n=1

Znh − Z(n−1)h

2

=N

n=1

E

Znh − Z(n−1)h

2+

N

n=1

EZ(n−1)h

Znh − Z(n−1)h

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Stochastic Processes for Finance

79

Stochastic integral and Itô’s lemma

Znh − Z(n−1)h

E

Znh − Z(n−1)h

2= V

Znh − Z(n−1)h

= nh− (n− 1)h = h

Nh = T.

N

n=1

EZ(n−1)h

Znh − Z(n−1)h

=

N

n=1

EZ(n−1)h

EZnh − Z(n−1)h

= 0

E(CN ) = T

[0;T ] N.

E(DN) CN DN

DN

gn =

n

s=1

θnhXnh −X(n−1)h

θnh F(n−1)h θ

σn−1 Yn F(n−1)h

BN

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Stochastic Processes for Finance

80

Stochastic integral and Itô’s lemma

E (B2N)

EB2

N

= E

(BN −BN−1 +BN−1)

2

= E(BN −BN−1)

2+ EB2

N−1

+ 2E (BN−1 (BN −BN−1))

= Eσ2N−1

ZNh − Z(N−1)h

2+ E

B2

N−1

+ 2E (BN−1)E (BN −BN−1)

= hEσ2N−1

+ E

B2

N−1

B.

ZNh − Z(N−1)h

σ2

N−1 E

ZNh − Z(N−1)h

2=

h.

EB2

N

= T

1

N

N

n=1

E(σ2n−1)

βN [0;T ]

βNs = σn−1 s ∈ [(n− 1)h;nh[

EB2

N

= E

T

0

βNs

2ds

βN σ.

E T

0(σs)

2 ds

< +∞ BN

Z ,A,FZ , P

σ FZ σ

E

T

0

σ2sds

< +∞

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Stochastic Processes for Finance

81

Stochastic integral and Itô’s lemma

σ [0;T ] Z T

0σsdZs

T

0

σsdZs = limN→+∞

N

n=1

σn−1

Znh − Z(n−1)h

n [0;T ] N [0;T ] [ti; ti+1[ tN = T

T

0

σsdZs = limN→+∞

N

i=1

σt−

Zt − Zt−

max(ti− ti−1) N → +∞. T.

u

0σsdZs, u ∈ T

(Iu(σ), u ∈ T ) . σ σn−1

σn θ,

T

0θsdZs

Z.

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Stochastic Processes for Finance

82

Stochastic integral and Itô’s lemma

X Y t

0XsdZs

t

0YsdZs t ≤ T

∀(a, b) ∈ R2, t

0(aXs + bYs)dZs = a

t

0XsdZs + b

t

0bYsdZs

∀(t, u) ∈ T × T , t < u, u

0XsdZs =

t

0XsdZs +

u

tXsdZs

∀(t, u) ∈ T × T , t < u, E u

0XsdZs |Ft

= t

0XsdZs

∀t ∈ T , E

t

0XsdZs

2= E

t

0X2

sds

X

u

0XsdZs, u ∈ T

t

0XsdZs L2

X

X0 = 0

Xn = Xn−1 + Yn = Xn−1 + n−1h+ σn−1

Znh − Z(n−1)h

X

Xt = X0 +

t

0

sds+

t

0

σsdZs

X s = (Xs, s) σs = σ(Xs, s)

dXt = (Xt, t)dt+ σ(Xt, t)dZt

X dZt

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Stochastic Processes for Finance

83

Stochastic integral and Itô’s lemma

X W σ ,

dWt = dt+ σdZt

W0 = 0)

t

0

dWs = Wt =

t

0

ds+

t

0

σdZs

=

t

0

ds+ σ

t

0

dZs

= t+ σZt

dZt). dZt Zt+dt − Zt dt > dt dt.

E(dZt) = 0 V (dZt) = dt

V (dZ2t ) = o(dt)

dZt.dt = o(dt)

E (dZtdZt) = 0 t1 = t2

Z Z∗

E (dZtdZ∗t ) = rtdt

V (dZtdZ∗t ) = o(dt)

dZt Zt+dt−Zt V (Zt − Zs) = t− s t > s.

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Stochastic Processes for Finance

84

Stochastic integral and Itô’s lemma

(dZt)2

Z

√dt −

√dt (dZt)

2

dt. dZt = O(

√dt) dZt.dt = O(dt

) = o(dt).

t1 = t2, dZt dZt

rt O(

√dt). O(dt).

O(dt2), o(dt).

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Stochastic Processes for Finance

85

Stochastic integral and Itô’s lemma

T K.

CT = max (XT −K; 0) .

C = (Ct, t ∈ T ) C0 . Ct = g(Xt, t) g R+×T R+. X C.

X Yt = exp(Xt)? Y Xt = ln(Yt)?

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Stochastic Processes for Finance

86

Stochastic integral and Itô’s lemma

f R2 R f (x0, t0)

f(x, t) = f(x0, t0) +∂f

∂x(x0, t0)(x− x0) +

∂f

∂t(x0, t0)(t− t0)

+1

2

∂2f

∂x2(x0, t0)(x− x0)

2 +1

2

∂2f

∂t2(x0, t0)(t− t0)

2

+∂2f

∂x∂t(x0, t0)(x− x0)(t− t0) + ε(x0, t0)

ε(x0, t0) ∼ o ((x− x0)2 + (t− t0)

2) . ε

x t− t0

√t− t0.

∂f∂x

(x0, t0)(x − x0)2

O(t− t0). ∂f∂t(x0, t0)(t− t0).

df(x0, t0) = f(x, t)−f(x0, t0) ; t−t0 = dt x−x0 =dx;

df(x0, t0) =∂f

∂x(x0, t0)dx+

∂f

∂t(x0, t0)dt+

1

2

∂2f

∂x2(x0, t0)(dx)

2

+1

2

∂2f

∂t2(x0, t0)(dt)

2 +∂2f

∂x∂t(x0, t0)dxdt+ ε(x0, t0)

x Xt X

dXt = (Xt, t)dt+ σ(Xt, t)dZt

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Stochastic Processes for Finance

87

Stochastic integral and Itô’s lemma

(Xt, t)

df(Xt, t) =∂f

∂x[(Xt, t)dt+ σ(Xt, t)dZt] +

∂f

∂tdt

+1

2

∂2f

∂x2[(Xt, t)dt+ σ(Xt, t)dZt]

2

+1

2

∂2f

∂t2(dt)2 +

∂2f

∂x∂t[(Xt, t)dt+ σ(Xt, t)dZt] dt+ ε(Xt, t)

∂f

∂t ∂f

∂x∂t o(dt)).

df(Xt, t) =∂f

∂x[(Xt, t)dt+ σ(Xt, t)dZt] +

∂f

∂tdt

+1

2

∂2f

∂x2[(Xt, t)dt+ σ(Xt, t)dZt]

2 + ε′(Xt, t)

ε′ = o(dt).

[(Xt, t)dt+ σ(Xt, t)dZt]2 ,

O(dt2)) dZtdt O(dt ))

dZ2t O(dt)

df(Xt, t) =∂f

∂x((Xt, t)dt+ σ(Xt, t)dZt) +

∂f

∂tdt

+1

2

∂2f

∂x2σ2(Xt, t)dt+ ε′′(Xt, t)

ε′′ = o(dt). f(Xt, t)

dt dZt

df(Xt, t) =

∂f

∂x(Xt, t) +

∂f

∂t+

1

2

∂2f

∂x2σ2(Xt, t)

dt+σ(Xt, t)

∂f

∂xdZt+ε′′(Xt, t)

ε(Xt, t) ε dt

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Stochastic Processes for Finance

88

Stochastic integral and Itô’s lemma

X

dXt = (Xt, t)dt+ σ(Xt, t)dZt

f : R2 → R Y Yt = f(Xt, t)

dYt =

∂f

∂x(Xt, t) +

∂f

∂t+

1

2

∂2f

∂x2σ2(Xt, t)

dt+ σ(Xt, t)

∂f

∂xdZt

Y

dYt = Y (Yt, t) dt+ σY (Yt, t) dZt

Y (Yt, t) =∂f

∂x(Xt, t) +

∂f

∂t+

1

2

∂2f

∂x2σ2(Xt, t)

σY (Yt, t) = σ(Xt, t)∂f

∂x

Y (Yt, t) f(Xt, t) σY (Yt, t)

W σ W Y Yt = f(Wt) = exp (Wt) . Y t ∂f

∂t= 0

Y

Y (Yt, t) =∂f

∂x+

∂f

∂t+

1

2

∂2f

∂x2σ2 = exp (Wt)

+

σ2

2

= Yt

+

σ2

2

σY (Yt, t) = σ∂f

∂x= σYt

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Stochastic Processes for Finance

89

Stochastic integral and Itô’s lemma

dYt

Yt=

+

σ2

2

dt+ σdZt

Y

Y

Y0 = 1

dYt = Ytdt+ σYtdZt

X Xt = g(Yt) = ln(Yt).

∂g

∂x(Yt, t) +

∂g

∂t+

1

2

∂2g

∂x2σ2(Yt, t) =

∂g

∂xYt +

1

2

∂2g

∂x2σ2Yt

= − σ2

2

σYt∂g

∂x= σ

dXt =

− σ2

2

dt+ σdZt

X

dXt = α(β −Xt)dt+ σdZt

Xt

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Stochastic Processes for Finance

90

Stochastic integral and Itô’s lemma

β) Xt

β. α

σ Xt. Z dt

√dt,

Z

dXt = α(β −Xt)dt+ σ

XtdZt

αβdt

β

β) Xt

β. α

σ Xt. Z dt

√dt,

Z

dXt = α(β −Xt)dt+ σ

XtdZt

αβdt

β

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Stochastic Processes for Finance

91

Stochastic integral and Itô’s lemma

W (, σ) , (,A,F , P ) F W. Z, Wt = t + σZt. Wt = ln(St) St Wt−Ws [s; t] . r Q W r.

P Q EP EQ P Q.

r = .

X ∼ N (0, 1) (,A, P ) Q

∀A ∈ A, Q(A) = EP

A exp

αX − α2

2

Q P X ∼ N (α, 1) Q.

EP

exp

αX − α

2

= exp

−α

2

EP [exp (αX)] exp (αX)

(0, α) ,

EP [exp (αX)] = exp

α

2

.

Q() = EP

exp

αX − α

2

= 1.

P Q

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Stochastic Processes for Finance

92

Stochastic integral and Itô’s lemma

EQ [X] =

XdQ =

X exp

αX − α2

2

dP

=

+∞

−∞x exp

αx− α2

2

fX(x)dx

=1√2π

+∞

−∞x exp

αx− α2

2

exp

−x2

2

dx

=1√2π

+∞

−∞x exp

−1

2(x− α)2

dx = α

fX X P. X Q, α exp

αX − α

2

Q P, dQ

dP.

Xt ∼ N (t, σ2t) t = EP (Xt) σ2t = VP (Xt) Xt rt =EQ(Xt) σ2t = VQ (Xt) .

X Xt = t+ σZt = t+ σ

√tYt

Z Yt ∼ N (0, 1) . Yt N (α, 1) α Q

EQ [Xt] = t+ σ√tα = rt

α

α =(r − )

√t

σ

dQ

dP= exp

(r − )

√t

σYt −

1

2

(r − )

√t

σ

2

= exp

−− r

σZt −

t

2

− r

σ

2

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Stochastic Processes for Finance

93

Stochastic integral and Itô’s lemma

λ = (λt, t ∈ [0;T ]) F L = (Lt, t ∈ [0;T ])

Lt = exp

− t

0

λsdZs −1

2

t

0

λ2sds

λ

EP

exp

1

2

T

0

λ2sds

< +∞

λ

L P− Z∗

Z∗t = Zt +

t

0

λsds

(,A,F , Q) Q

dQ

dP= LT

λ L

Lt = exp

−λZt −

λ2

2t

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Stochastic Processes for Finance

94

Stochastic integral and Itô’s lemma

dSt = Stdt+ σStdZt

St = S0 exp

− σ2

2

t+ σZt

exp (−rt)St = S0 exp

− r − σ2

2

t+ σZt

St = S0 exp

−σ2

2t+ σZ∗

t

Z∗t = Zt +

−rσ

t.

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Stochastic Processes for Finance

95

Stochastic integral and Itô’s lemma

σ

F (,A, P ) Z.

X0 = c

dXt = (Xt, t) dt+ σ (Xt, t) dZt

c c

X [0;T ]

X F σ

T

0

| (Xt, t)| dt < +∞ T

0

σ2 (Xt, t) dt < +∞

X

Xt = X0 +

t

0

(Xs, s) ds+

t

0

σ (Xs, s) dZs

σ

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Stochastic Processes for Finance

96

Stochastic integral and Itô’s lemma

P X

F , EP

T

0X2

t dt

< +∞.

m > 0 ∀t ∈ [0;T ] , ∀ (x, y) ∈ R2

max (|(x, t)− (y, t)| ; |σ(x, t)− σ(y, t)|) ≤ m |x− y|(x, t)2 + σ(x, t)2 ≤ m

1 + x2

(x, t)

X0 Ft t.

σ t. σ X (1 + x2)

.

(x, t) = exp(x).

Z (X,Z) σ

Xt

c.

σ t, X σ.

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Stochastic Processes for Finance

97

Stochastic integral and Itô’s lemma

X0 = c

dXt = aXtdt+ σtdZt

a

Xt = c exp(at) +

t

0

exp [a (t− s)] σsdZs

Xt exp(−at) = c+

t

0

exp (−as) σsdZs

Y,

Y0 = c

dYt = exp(−at)σtdZt

exp(at)Yt = f (Yt, t) , f

∂f

∂t= a exp(at)Yt

∂f

∂Yt

= exp(at)

∂2f

∂Y 2t

= 0

df (Yt, t) = a exp(at)Ytdt+ exp(at) exp(−at)σtdZt

= a exp(at)Ytdt+ σtdZt

Yt exp(−at)Xt

dXt = aXtdt+ σtdZt

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Stochastic Processes for Finance

98

Stochastic integral and Itô’s lemma

X0 = c

dXt = a(t)Xtdt+ σtdZt

X

Xt = γt

c+

t

0

γ−1s σsdZs

γ

f′(t) = a(t)f(t)

Y

Y0 = y0

dYt = α (β − Yt) dt+ σdZt

Xt = (Yt − β) exp(αt) = f(Yt, t); f

∂f

∂t= α exp(αt) (Yt − β)

∂f

∂Yt= exp(αt)

∂2f

∂Y 2t

= 0

dXt = [α (Yt − β) exp(αt) + exp(αt)α (β − Yt)] dt+ σ exp(αt)dZt

= σ exp(αt)dZt

X0 = y0 − β,

Xt = y0 − β + σ

t

0

exp(αs)dZs

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Stochastic Processes for Finance

99

Stochastic integral and Itô’s lemma

X Y

Yt = β +Xt exp(−αt)

Yt = β + exp(−αt)

y0 − β + σ

t

0

exp(αs)dZs

= β (1− exp(−αt)) + y0 exp(−αt) + σ

t

0

exp(−α(t− s))dZs

Yt

EP [Yt |Y0 = y0 ] = β (1− exp(−αt)) + y0 exp(−αt)

VP [Yt |Y0 = y0 ] = σ2

t

0

exp(−2α(t− s))ds

=σ2

2α[1− exp (−2αt)]

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Stochastic Processes for Finance

100

Bibliography

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Stochastic Processes for Finance

101

Bibliography

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Stochastic Processes for Finance

102

Bibliography

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Stochastic Processes for Finance

103

Index

Index

Brownian motion, 57 general, 63 geometric, 85 Markov process, 59 martingale, 59 path simulation, 65 stopping time, 67 transformation, 60

Doob decomposition, 25, 51 martingale, 24

Filtration, 9, 44 complete, 44 filtered probability space, 44 natural, 10, 44 right-continuous, 44 Girsanov theorem, 89 application, 89 Novikov condition, 89

Itô lemma, 84 application, 84 Taylor series expansion, 82 process, 48 diffusion coefficient, 49 drift, 49

Landau notations, 55

Markov chain, 15 accessible, 17 aperiodic, 18 Chapman-Kolmogorov equations, 16 communicating class, 17 communication, 17 homogeneous, 15 irreducible, 18 periodicity, 18

positively recurrent, 19 recurrent, 19 stationary distribution, 20 transient, 19 no memory process, 48 process, 15, 48 Brownian motion, 59 transition matrix, 15Martingale, 21, 49 Brownian motion, 59 Doob, 24 submartingale, 21, 49 super-martingale, 21, 49Modi cation stochastic process, 43

Novikov condition, 89

Path, 40 càdlàg, 41 càglàd, 43 continuous, 40 Brownian motion, 58 LCRL, 43 nowhere differentiable, 59 RCLL, 41Poisson distribution, 47 process, 47Probability transition, 15

Snell envelope, 36Stochastic differential equation, 91 linear, 93 solution conditions, 92 de nition, 91 Markov, 92Stochastic integral, 71 calculation rules, 79 definition, 76 properties, 78 stochastic differential, 78

Stochastic Processes for Finance

104

Index

Stochastic process adapted, 10, 44 Brownian motion, 57 continuous-time, 40 diffusion, 48 diffusion coefficient, 49 discrete-time, 6 drift, 49 increments independent, 45 stationary, 45 indistinguishable, 43 Itô, 48 Markov, 15, 48 modification, 43 Ornstein-Uhlenbeck, 85 path, 40 Poisson, 47 predictable, 12, 45 random walk, 22, 46, 51 square root, 86 stopped process, 33, 37 trajectory, 40 Wiener, 57Stopping time, 32, 37, 65 American option, 35 optional stopping theorem, 32Strategy doubling, 34 portfolio, 28 self- nancing, 28

Transition matrix, 15 probability, 15

Wiener process, 57 general, 63