Malliavin Calculus With Applications to Stochastic Partial Differential Equations

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Format: Hardcover
Pub. Date: 2005-08-17
Publisher(s): EFPL Press
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Summary

Developed in the 1970s to study the existence and smoothness of density for the probability laws of random vectors, Malliavin calculus--a stochastic calculus of variation on the Wiener space--has proven fruitful in many problems in probability theory, particularly in probabilistic numerical methods in financial mathematics.This book presents applications of Malliavin calculus to the analysis of probability laws of solutions to stochastic partial differential equations driven by Gaussian noises that are white in time and coloured in space. The first five chapters introduce the calculus itself based on a general Gaussian space, going from the simple, finite-dimensional setting to the infinite-dimensional one. The final three chapters discuss recent research on regularity of the solution of stochastic partial differential equations and the existence and smoothness of their probability laws.About the author:Marta Sanz-Solé is Professor at the Faculty of Mathematics, University of Barcelona. She is a leading member of the research group on stochastic analysis at Barcelona, and in 1998 she received the Narcis Monturiol Award of Scientific and Technological Excellence from the autonomous government of Catalonia.

Table of Contents

Introduction v
CHAPTER 1 Integration by Parts and Absolute Continuity of Probability Laws 1(6)
CHAPTER 2 Finite Dimensional Malliavin Calculus 7(10)
2.1 The Ornstein-Uhlenbeck operator
7(5)
2.2 The adjoint of the differential
12(1)
2.3 An integration by parts formula: Existence of a density
13(3)
2.4 Exercises
16(1)
CHAPTER 3 The Basic Operators of Malliavin Calculus 17(28)
3.1 The Ornstein-Uhlenbeck operator
18(4)
3.2 The derivative operator
22(4)
3.3 The integral or divergence operator
26(1)
3.4 Differential calculus
27(6)
3.5 Calculus with multiple Wiener integrals
33(6)
3.6 Local property of the operators
39(2)
3.7 Exercises
41(4)
CHAPTER 4 Representation of Wiener Functionals 45(16)
4.1 The Itô integral and the divergence operator
46(2)
4.2 The Clark-Ocone formula
48(1)
4.3 Generalized Clark-Ocone formula
49(5)
4.4 Application to option pricing
54(5)
4.5 Exercises
59(2)
CHAPTER 5 Criteria for Absolute Continuity and Smoothness of Probability Laws 61(8)
5.1 Existence of a density
61(5)
5.2 Smoothness of the density
66(3)
CHAPTER 6 Stochastic Partial Differential Equations Driven by Spatially Homogeneous Gaussian Noise 69(24)
6.1 Stochastic integration with respect to coloured noise
69(10)
6.2 Stochastic partial differential equations driven driven by a coloured noise
79(11)
6.3 Exercises
90(3)
CHAPTER 7 Malliavin Regularity of Solutions of SPDE's 93(28)
7.1 Exercises
120(1)
CHAPTER 8 Analysis of the Malliavin Matrix of Solutions of SPDE's 121(32)
8.1 One dimensional case
121(14)
8.2 Examples
135(11)
8.3 Multidimensional case
146(7)
Definitions of spaces 153(2)
Bibliography 155

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