An Introduction to Malliavin calculus and its applications

Similar documents
On a Fractional Stochastic Landau-Ginzburg Equation

6. Stochastic calculus with jump processes

Monotonic Solutions of a Class of Quadratic Singular Integral Equations of Volterra type

Example on p. 157

Lecture 20: Riccati Equations and Least Squares Feedback Control

4 Sequences of measurable functions

Hamilton- J acobi Equation: Explicit Formulas In this lecture we try to apply the method of characteristics to the Hamilton-Jacobi equation: u t

Utility maximization in incomplete markets

Backward stochastic dynamics on a filtered probability space

Hamilton Jacobi equations

Simulation of BSDEs and. Wiener Chaos Expansions

t 2 B F x,t n dsdt t u x,t dxdt

Generalized Snell envelope and BSDE With Two general Reflecting Barriers

MODULE 3 FUNCTION OF A RANDOM VARIABLE AND ITS DISTRIBUTION LECTURES PROBABILITY DISTRIBUTION OF A FUNCTION OF A RANDOM VARIABLE

23.5. Half-Range Series. Introduction. Prerequisites. Learning Outcomes

Simulation of BSDEs and. Wiener Chaos Expansions

The Asymptotic Behavior of Nonoscillatory Solutions of Some Nonlinear Dynamic Equations on Time Scales

Convergence of the Neumann series in higher norms

2 Some Property of Exponential Map of Matrix

DISCRETE GRONWALL LEMMA AND APPLICATIONS

Uniqueness of solutions to quadratic BSDEs. BSDEs with convex generators and unbounded terminal conditions

Cash Flow Valuation Mode Lin Discrete Time

Math 334 Fall 2011 Homework 11 Solutions

arxiv: v1 [math.ca] 15 Nov 2016

LECTURE 1: GENERALIZED RAY KNIGHT THEOREM FOR FINITE MARKOV CHAINS

arxiv: v1 [math.fa] 9 Dec 2018

arxiv: v1 [math.pr] 19 Feb 2011

Ann. Funct. Anal. 2 (2011), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:

Stochastic delay differential equations driven by fractional Brownian motion with Hurst parameter H > 1 2

Undetermined coefficients for local fractional differential equations

Time discretization of quadratic and superquadratic Markovian BSDEs with unbounded terminal conditions

Oscillation of an Euler Cauchy Dynamic Equation S. Huff, G. Olumolode, N. Pennington, and A. Peterson

Lie Derivatives operator vector field flow push back Lie derivative of

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation

Variational Iteration Method for Solving System of Fractional Order Ordinary Differential Equations

Ordinary Differential Equations

Hamilton- J acobi Equation: Weak S olution We continue the study of the Hamilton-Jacobi equation:

Loss of martingality in asset price models with lognormal stochastic volatility

Research Article Existence and Uniqueness of Periodic Solution for Nonlinear Second-Order Ordinary Differential Equations

EXISTENCE AND UNIQUENESS OF SOLUTIONS TO THE BACKWARD STOCHASTIC LORENZ SYSTEM

A FAMILY OF MARTINGALES GENERATED BY A PROCESS WITH INDEPENDENT INCREMENTS

MATH 4330/5330, Fourier Analysis Section 6, Proof of Fourier s Theorem for Pointwise Convergence

On Oscillation of a Generalized Logistic Equation with Several Delays

Dual Representation as Stochastic Differential Games of Backward Stochastic Differential Equations and Dynamic Evaluations

THE WAVE EQUATION. part hand-in for week 9 b. Any dilation v(x, t) = u(λx, λt) of u(x, t) is also a solution (where λ is constant).

Singular control of SPDEs and backward stochastic partial diffe. reflection

A proof of Ito's formula using a di Title formula. Author(s) Fujita, Takahiko; Kawanishi, Yasuhi. Studia scientiarum mathematicarum H Citation

A remark on the H -calculus

Fréchet derivatives and Gâteaux derivatives

CERTAIN CLASSES OF SOLUTIONS OF LAGERSTROM EQUATIONS

Semilinear Kolmogorov equations and applications to stochastic optimal control

Chapter 6. Systems of First Order Linear Differential Equations

Finish reading Chapter 2 of Spivak, rereading earlier sections as necessary. handout and fill in some missing details!

Existence and uniqueness of solution for multidimensional BSDE with local conditions on the coefficient

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance.

Positive continuous solution of a quadratic integral equation of fractional orders

THE GENERALIZED PASCAL MATRIX VIA THE GENERALIZED FIBONACCI MATRIX AND THE GENERALIZED PELL MATRIX

Math 2142 Exam 1 Review Problems. x 2 + f (0) 3! for the 3rd Taylor polynomial at x = 0. To calculate the various quantities:

Backward doubly stochastic di erential equations with quadratic growth and applications to quasilinear SPDEs

14 Autoregressive Moving Average Models

1 Solutions to selected problems

Solutions to Assignment 1

EMS SCM joint meeting. On stochastic partial differential equations of parabolic type

CONTRIBUTION TO IMPULSIVE EQUATIONS

MATH 128A, SUMMER 2009, FINAL EXAM SOLUTION

Chapter 2. First Order Scalar Equations

Chapter 3 Boundary Value Problem

The Optimal Stopping Time for Selling an Asset When It Is Uncertain Whether the Price Process Is Increasing or Decreasing When the Horizon Is Infinite

MATH 5720: Gradient Methods Hung Phan, UMass Lowell October 4, 2018

ODEs II, Lecture 1: Homogeneous Linear Systems - I. Mike Raugh 1. March 8, 2004

A Primal-Dual Type Algorithm with the O(1/t) Convergence Rate for Large Scale Constrained Convex Programs

A Sharp Existence and Uniqueness Theorem for Linear Fuchsian Partial Differential Equations

Asymptotic instability of nonlinear differential equations

Sobolev-type Inequality for Spaces L p(x) (R N )

arxiv: v1 [math.pr] 21 May 2010

System of Linear Differential Equations

POSITIVE SOLUTIONS OF NEUTRAL DELAY DIFFERENTIAL EQUATION

Weak error analysis via functional Itô calculus

Heat kernel and Harnack inequality on Riemannian manifolds

Math Final Exam Solutions

Differential Equations

Transformations of measure on infinite-dimensional vector spaces

Attractors for a deconvolution model of turbulence

CHARACTERIZATION OF REARRANGEMENT INVARIANT SPACES WITH FIXED POINTS FOR THE HARDY LITTLEWOOD MAXIMAL OPERATOR

2. Nonlinear Conservation Law Equations

A NOTE ON THE SMOLUCHOWSKI-KRAMERS APPROXIMATION FOR THE LANGEVIN EQUATION WITH REFLECTION

Lecture 10: The Poincaré Inequality in Euclidean space

On Gronwall s Type Integral Inequalities with Singular Kernels

L p -L q -Time decay estimate for solution of the Cauchy problem for hyperbolic partial differential equations of linear thermoelasticity

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...

Solutions of Sample Problems for Third In-Class Exam Math 246, Spring 2011, Professor David Levermore

A HARDY TYPE GENERAL INEQUALITY IN L p( ) (0, 1) WITH DECREASING EXPONENT

EXISTENCE OF S 2 -ALMOST PERIODIC SOLUTIONS TO A CLASS OF NONAUTONOMOUS STOCHASTIC EVOLUTION EQUATIONS

Section 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients

Lecture 6: Wiener Process

Homework sheet Exercises done during the lecture of March 12, 2014

Markov Processes and Stochastic Calculus

Stable approximations of optimal filters

THE MYSTERY OF STOCHASTIC MECHANICS. Edward Nelson Department of Mathematics Princeton University

Predator - Prey Model Trajectories and the nonlinear conservation law

Transcription:

An Inroducion o Malliavin calculus and is applicaions Lecure 5: Smoohness of he densiy and Hörmander s heorem David Nualar Deparmen of Mahemaics Kansas Universiy Universiy of Wyoming Summer School 214 David Nualar (Kansas Universiy) May 214 1 / 31

Le F = (F 1,..., F m ) be such ha F i D 1,2 for i = 1,..., m. The Malliavin marix of F is γ F = ( DF i, DF j H ) 1 i,j m. Theorem (Crierion for absolue coninuiy) If de γ F > a.s., hen he law of F is absoluely coninuous wih respec o he Lebesgue measure on R m. Theorem (Crierion for smoohness of he densiy) If F i D and E[(de γ F ) p ] < for all p 1, hen he law of F possesses and infiniely differeniabiliy densiy. David Nualar (Kansas Universiy) May 214 2 / 31

d-dimensional Brownian moion (Ω, F, P) is he canonical probabiliy space associaed wih a d-dimensional Brownian moion {W i (), [, T ], 1 i d} : i) Ω = C ([, T ]; R d ). ii) P is he law of he d-dimensional Brownian moion. iii) F is he compleion of he Borel σ-field of Ω wih respec o P. The Hilber space here is H = L 2 ([, T ]; R d ), and for any h H, W (h) is he Wiener inegral T W (h) = hsdw i s. i i=1 The derivaive DF of a random variable F D 1,2 will be a d-dimensional process denoed by {D i F, [, T ], i = 1,..., d}. For example D i sw j = δ i,j 1 [,] (s). David Nualar (Kansas Universiy) May 214 3 / 31

Diffusion processes Consider he m-dimensional sochasic differenial equaion X = x + j=1 A j (X s )dws j + B(X s )ds, (1) where A j, B : R m R m, 1 j d are measurable funcions. We know ha under he Lipschiz condiion ( max Aj (x) A j (y), B(x) B(y) ) K x y, (2) j for all x, y R m here exiss a unique soluion X = {X, [, T ]} of Equaion (1). David Nualar (Kansas Universiy) May 214 4 / 31

Differeniabiliy of he soluion Proposiion Suppose ha he coefficiens A j, B are in C 1 b (Rm ; R m ). Then, for all [, T ] and i = 1,..., m, X i D 1, and for r m Dr j X = A j (X r ) + k A l (X s )D j r X k s dw l s + m k=1 l=1 r k=1 r k B(X s )D j r X k s ds. (3) If he coefficiens are infiniely differeniable in he space variable and heir parial derivaives of all orders are uniformly bounded, hen X i () belongs o D. David Nualar (Kansas Universiy) May 214 5 / 31

Proof : To simplify we assume B =. Consider he Picard approximaions given by X () = x and X (n+1) = x + j=1 A j (X (n) s )dw j s. if n. We will prove he following claim by inducion on n : Claim : X (n),i and for some consans c 1, c 2. D 1, for all i,, and for all p > 1 we have ( ψ n () := sup E r ) sup D r X (n) s p < (4) s [r,] ψ n+1 () c 1 + c 2 ψ n (s)ds, (5) David Nualar (Kansas Universiy) May 214 6 / 31

Clearly, he claims holds for n =. Suppose i is rue for n. Applying propery (2) of he divergence and chain rule, for any r, i = 1,..., m and l = 1,..., d, we ge Dr l X (n+1),i = Dr l [ A i (n) j (X s )dws] j = δ l,j A i l(x (n) r ) + = δ l,j A i l(x (n) r ) + r m k=1 D l r [A i j r (n) (X s )]dws j k A j (X (n) s )Dr l X (n),k s dws. j David Nualar (Kansas Universiy) May 214 7 / 31

From hese equaliies and condiion (4) we see ha X (n+1),i D 1,, and we obain using Burkholder s inequaliy ( ) [ ] ( E sup D r X (n+1) s p c p γ p + T p 1 K p E Dr j X (n) s p) ds, (6) r s r where γ p = sup E( sup A j (X (n) ) p ) <. n,j T So (4) and (5) hold for n + 1 and he claim is proved. David Nualar (Kansas Universiy) May 214 8 / 31

We know ha as n ends o infiniy. E ( ) sup X (n) s X s p s T By Gronwall s lemma applied o (5) we deduce ha he derivaives of he sequence X (n),i are bounded in L p (Ω; H) uniformly in n for all p 2. This implies ha he random variables X i belong o D 1,. Finally, applying he operaor D o Equaion (1) we deduce he linear sochasic differenial equaion (3) for he derivaive of X i. This complees he proof of he proposiion. David Nualar (Kansas Universiy) May 214 9 / 31

Consider he m m marix-valued process defined by Y = I + l=1 A l (X s )Y s dws l + B(X s )Y s ds, where A l denoes he m m Jacobian marix of he funcion A l, ha is, ( A l ) i j = j A i l. In he same way, B denoes he m m Jacobian marix of B. If he coefficiens of Equaion (1) are of class C 1+α, α >, hen here is a version of he soluion X (x ) o his equaion ha is coninuously differeniable in x, and Y is he Jacobian marix X x : Y = X x. David Nualar (Kansas Universiy) May 214 1 / 31

Recall ha Y = I + l=1 A l (X s )Y s dws l + B(X s )Y s ds. Proposiion For any [, T ] he marix Y is inverible. Is inverse Z saisfies Z = I l=1 Z s A l (X s )dw l s Z s [ B(X s ) ] A l (X s ) A l (X s ) ds. l=1 David Nualar (Kansas Universiy) May 214 11 / 31

Proof : By means of Iô s formula, one can check ha Z Y = Y Z = I, which implies ha Z = Y 1. In fac, Z Y = I + l=1 l=1 Z s A l (X s )Y s dws l + Z s B(X s )Y s ds Z s A l (X s )Y s dw l s Z s [ B(X s ) and similarly we show ha Y Z = I. ] A l (X s ) A l (X s ) Y s ds l=1 ( ) Z s A l (X s ) A l (X s ) Y s ds = I, l=1 David Nualar (Kansas Universiy) May 214 12 / 31

Lemma The m d marix (D r X ) i j = D j r X i can be expressed as D r X = Y Y 1 r A(X r ). (7) Proof : I suffices o check ha he process Φ,r := Y Yr 1 A(X r ), saisfies In fac, Φ,r = A(X r ) + l=1 A(X r ) + + r l=1 r A l (X s )Φ s,r dws l + B(X s )Φ s,r ds. r = A(X r ) + [Y Y r ] Y 1 r r A l (X s ) { Y s Yr 1 A(X r ) } dws l B(X s ) { Y s Yr 1 A(X r ) } ds A(X r ) = Y Yr 1 A(X r ). David Nualar (Kansas Universiy) May 214 13 / 31

Consider he Malliavin marix of X, denoed by γ X := Q, given by ha is Equaion (7) leads o where C = Y 1 s Q i,j = Q = l=1 D l sx i D l sx j ds, (D s X )(D s X ) T ds. Q = Y C Y T, (8) A(X s )A T (X s ) ( Y 1 ) T ds = and σ is he m m diffusion marix σ = AA T. s Y 1 s σ(x s ) ( Y 1 ) T ds, Taking ino accoun ha Y is inverible, he nondegeneracy of he marix Q will depend only on he nondegeneracy of he marix C, which is called he reduced Malliavin marix. s David Nualar (Kansas Universiy) May 214 14 / 31

Absolue coninuiy under ellipiciy condiions Consider he sopping ime defined by S = inf{ > : de σ(x ) } T. Theorem (Bouleau and Hirsch 85) Le {X(), [, T ]} be a diffusion process wih C 1 and Lipschiz coefficiens. Then for any < T he law of X() condiioned by { > S} is absoluely coninuous wih respec o he Lebesgue measure on R m. David Nualar (Kansas Universiy) May 214 15 / 31

Proof : I suffices o show ha de C > a.s. on he se {S < }. Suppose > S. For any u R m wih u = 1 we can wrie u T C u = u T Y 1 s σ(x s )(Y 1 ) T uds ( ) inf v T σ(x s )v v =1 s (Ys 1 ) T u 2 ds. ( Noice ha inf v =1 v T σ(x s )v ) is he smalles eigenvalue of σ(x s ) which is sricly posiive in an open inerval conained in [, ] by he definiion of he sopping ime S and because > S. On he oher hand, (Ys 1 ) T u u Y s 1. Therefore we obain u T C u k u 2, for some posiive random variable k >, which implies ha he marix C is inverible. This complees he proof. David Nualar (Kansas Universiy) May 214 16 / 31

Regulariy of he densiy under Hörmander s condiions Assume ha he coefficiens are infiniely differeniable wih bounded derivaives of all orders. Then, X i belong o D. Consider he following vecor fields on R m : A j = B = m i=1 m i=1 A i j (x) x i, j = 1,..., d, B i (x) x i. The Lie bracke beween he vecor fields A j and A k is defined by where [A j, A k ] = A j A k A k A j = A j A k A k A j, A j A k = m A l j la i k. x i i,l=1 David Nualar (Kansas Universiy) May 214 17 / 31

Se A = B 1 2 A l A l. The vecor field A appears when we wrie he sochasic differenial equaion (1) in erms of he Sraonovich inegral insead of he Iô inegral : X = x + A j (X s ) dws j + A (X s )ds. j=1 Hörmander s condiion : The vecor space spanned by he vecor fields A 1,..., A d, [A i, A j ], i d, 1 j d, [A i, [A j, A k ]], i, j, k d,... a poin x is R m. For insance, if m = d = 1, A 1 1 (x) = a(x), and A1 (x) = b(x), hen Hörmander s condiion means ha a(x ) or a n (x )b(x ) for some n 1. l=1 David Nualar (Kansas Universiy) May 214 18 / 31

Theorem Assume ha Hörmander s condiion (H) holds. Then for any > he random vecor X has an infiniely differeniable densiy. This resul can be considered as a probabilisic version of Hörmander s heorem on he hypoellipiciy of second-order differenial operaors. David Nualar (Kansas Universiy) May 214 19 / 31

Lemma Le {Z, } be a real-valued, adaped coninuous process such ha Z = z. Suppose ha here exiss α > such ha for all p 1 and [, T ], ( E ) sup Z s z p C p,t pα. s Then, for all p 1 and (, T ], [ ( ) p ] E Z s ds <. David Nualar (Kansas Universiy) May 214 2 / 31

Proof : For any < ɛ < z 2 we have ( P ) Z s ds < ɛ P which implies he desired resul. ( ) 2ɛ/ z Z s ds < ɛ ( ) P sup Z s z > z s 2ɛ/ z 2 ( ) 2p C p,t 2ɛ pα z p, z David Nualar (Kansas Universiy) May 214 21 / 31

Lemma (Norris) Consider a coninuous semimaringale of he form where Y = y + a(s)ds + a() = α + β(s)ds + i=1 i=1 u i (s)dw i s, γ i (s)dw i s, and c = E ( sup T ( β() + γ() + a() + u() ) p) < for some p 2. Fix q > 8. Then, for all r < q 8 27 here exiss ɛ such ha for all ɛ ɛ we have ( T ) T P Y 2 d < ɛ q, ( a() 2 + u() 2 )d ɛ c 1 ɛ rp. David Nualar (Kansas Universiy) May 214 22 / 31

Skech of he proof of Hörmander s heorem : Sep 1 We need o show ha for all > and all p 2, E[(de Q ) p ] <, where Q is he Malliavin marix of X. Taking ino accoun ha ( de ) E Y 1 p + de Y p <, i suffices o show ha E[(de C ) p ] < for all p 2. Sep 2 have Fix >. Then he problem is reduced o show ha for all p 2 we sup P{v T C v ɛ} ɛ p v =1 for any ɛ ɛ (p), where he quadraic form associaed o he marix C is given by v T C v = v, Ys 1 A j (X s ) 2 ds. (9) j=1 David Nualar (Kansas Universiy) May 214 23 / 31

Sep 3 Fix a smooh funcion V and use Iô s formula o compue he differenial of Y 1 V (X ) d ( ) Y 1 V (X ) = Y 1 +Y 1 [A k, V ](X )dw k k=1 { [A, V ] + 1 2 } [A k, [A k, V ]] (X )d. (1) k=1 David Nualar (Kansas Universiy) May 214 24 / 31

Sep 4 We inroduce he following ses of vecor fields : Σ = {A 1,..., A d }, Σ n = {[A k, V ], k =,..., d, V Σ n 1 } if n 1, Σ = n=σ n, and Σ = Σ, Σ n = {[A k, V ], k = 1,..., d, V Σ n 1; [A, V ] + 1 [A j, [A j, V ]], V Σ 2 n 1} if n 1, Σ = n=σ n. j=1 David Nualar (Kansas Universiy) May 214 25 / 31

i) We denoe by Σ n (x) (resp. Σ n(x)) he subse of R m obained by freezing he variable x in he vecor fields of Σ n (resp. Σ n). ii) Clearly, he vecor spaces spanned by Σ(x ) or by Σ (x ) coincide, and under Hörmander s condiion his vecor space is R m. iii) Therefore, here exiss an ineger j such ha he linear span of he se of vecor fields j j= Σ j (x) a poin x has dimension m. iv) As a consequence here exis consans R > and c > such ha j v, V (y) 2 c, (11) j= V Σ j for all v and y wih v = 1 and y x < R. David Nualar (Kansas Universiy) May 214 26 / 31

Sep 5 For any j =, 1,..., j we pu m(j) = 2 4j and we define he se E j = v, Ys 1 V (X s ) 2 ds ɛ m(j). V Σ j Noice ha {v T C v ɛ} = E because m() = 1. Consider he decomposiion E (E E c 1 ) (E 1 E c 2 ) (E j 1 E c j ) F, where F = E E 1 E j. Then for any uni vecor v we have P{v T C v ɛ} = P(E ) P(F) + We are going o esimae each erm of his sum. j j= P(E j E c j+1 ). David Nualar (Kansas Universiy) May 214 27 / 31

Sep 6 Le us firs esimae P(F ). By he definiion of F we obain j P(F ) P v, Ys 1 V (X s ) 2 ds (j + 1)ɛ m(j ). j= V Σ j Then, aking ino accoun (11) we can apply he previous lemma o he process j Z s = v, Ys 1 V (X s ) 2, j= V Σ j we obain E j j= V Σ j v, Y 1 s V (X s ) 2 ds p <. Therefore, for any p 1 he exiss ɛ such ha for any ɛ < ɛ. P(F ) ɛ p David Nualar (Kansas Universiy) May 214 28 / 31

Sep 7 For any j =,..., j he probabiliy of he even E j Ej+1 c is bounded by he sum wih respec o V Σ j of he probabiliy ha he wo following even happens and k=1 + v, Ys 1 [A k, V ](X s ) 2 ds v, Y 1 s v, Ys 1 V (X s ) 2 ds ɛ m(j) [A, V ] + 1 2 [A j, [A j, V ]] (X s ) j=1 where n(j) denoes he cardinaliy of he se Σ j. 2 ds > ɛm(j+1) n(j), David Nualar (Kansas Universiy) May 214 29 / 31

Consider he coninuous semimaringale { v, Y 1 s V (X s ), s }. From (1) we see ha he quadraic variaion of his semimaringale is equal o k=1 s v, Yσ 1 [A k, V ](X σ ) 2 dσ, and he bounded variaion componen is s v, Yσ 1 [A, V ] + 1 [A j, [A j, V ]] 2 (X σ) dσ. Taking ino accoun ha 8m(j + 1) < m(j), from Norris Lemma applied o he semimaringale Y s = v T Ys 1 V (X s ) we ge ha for any p 1 here exiss ɛ > such ha P(E j Ej+1 c ) ɛp, for all ɛ ɛ. The proof of he heorem is now complee. j=1 David Nualar (Kansas Universiy) May 214 3 / 31

Example Consider he following example. dx 1 = dw 1 + sin X 2 dw 2, dx 2 = 2X 1 dw 1 + X 1 dw 2 wih iniial condiion x =. In his case he diffusion marix [ 1 + sin 2 ] x σ(x) = 2 x 1 (2 + sin x 2 ) x 1 (2 + sin x 2 ) 5x1 2 [ degeneraes along ] he line x 1 =. The Lie bracke [A 1, A 2 ] is equal o 2x1 cos x 2. Therefore, he vecor fields A 1 2 sin x 1 and [A 1, A 2 ] a x = span R 2 2 and Hörmander s condiion holds. So X has a C densiy for any >. David Nualar (Kansas Universiy) May 214 31 / 31