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2006 | Buch

The Malliavin Calculus and Related Topics

verfasst von: David Nualart

Verlag: Springer Berlin Heidelberg

Buchreihe : Probability and its Applications

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Über dieses Buch

There have been ten years since the publication of the ?rst edition of this book. Since then, new applications and developments of the Malliavin c- culus have appeared. In preparing this second edition we have taken into account some of these new applications, and in this spirit, the book has two additional chapters that deal with the following two topics: Fractional Brownian motion and Mathematical Finance. The presentation of the Malliavin calculus has been slightly modi?ed at some points, where we have taken advantage of the material from the lecturesgiveninSaintFlourin1995(seereference[248]).Themainchanges and additional material are the following: In Chapter 1, the derivative and divergence operators are introduced in the framework of an isonormal Gaussian process associated with a general 2 Hilbert space H. The case where H is an L -space is trated in detail aft- s,p wards (white noise case). The Sobolev spaces D , with s is an arbitrary real number, are introduced following Watanabe’s work. Chapter2includesageneralestimateforthedensityofaone-dimensional random variable, with application to stochastic integrals. Also, the c- position of tempered distributions with nondegenerate random vectors is discussed following Watanabe’s ideas. This provides an alternative proof of the smoothness of densities for nondegenerate random vectors. Some properties of the support of the law are also presented.

Inhaltsverzeichnis

Frontmatter
Introduction
Abstract
The Malliavin calculus (also known as the stochastic calculus of variations) is an infinite-dimensional differential calculus on the Wiener space. It is tailored to investigate regularity properties of the law of Wiener functionals such as solutions of stochastic differential equations. This theory was initiated by Malliavin and further developed by Stroock, Bismut, Watanabe, and others. The original motivation, and the most important application of this theory, has been to provide a probabilistic proof of Hörmander’s “sum of squares” theorem.
David Nualart
Analysis on the Wiener space
Abstract
In this chapter we study the differential calculus on a Gaussian space. That is, we introduce the derivative operator and the associated Sobolev spaces of weakly differentiable random variables. Then we prove the equivalence of norms established by Meyer and discuss the relationship between the basic differential operators: the derivative operator, its adjoint (which is usually called the Skorohod integral), and the Ornstein-Uhlenbeck operator.
David Nualart
Regularity of probability laws
Abstract
In this chapter we apply the techniques of the Malliavin calculus to study the regularity of the probability law of a random vector defined on a Gaussian probability space. We establish some general criteria for the absolute continuity and regularity of the density of such a vector. These general criteria will be applied to the solutions of stochastic differential equations and stochastic partial differential equations driven by a space-time white noise.
David Nualart
Anticipating stochastic calculus
Abstract
As we have seen in Chapter 2, the Skorohod integral is an extension of the Itô integral that allows us to integrate stochastic processes that are not necessarily adapted to the Brownian motion. The adaptability assumption is replaced by some regularity condition. It is possible to develop a stochastic calculus for the Skorohod integral which is similar in some aspects to the classical Itô calculus. In this chapter we present the fundamental facts about this stochastic calculus, and we also discuss other approaches to the problem of constructing stochastic integrals for nonadapted processes (approximation by Riemann sums, development in a basis of L2([0, 1]), substitution methods). The last section discusses noncausal stochastic differential equations formulated using anticipating stochastic integrals.
David Nualart
Transformations of the Wiener measure
Abstract
In this chapter we discuss different extensions of the classical Girsanov theorem to the case of a transformation of the Brownian motion induced by a nonadapted process. This generalized version of Girsanov’s theorem will be applied to study the Markov property of solutions to stochastic differential equations with boundary conditions.
David Nualart
Fractional Brownian motion
Abstract
The fractional Brownian motion is a self-similar centered Gaussian process with stationary increments and variance equals t2H, where H is a parameter in the interval (0, 1). For H = ½ this process is a classical Brownian motion. In this chapter we will present the application of the Malliavin Calculus to develop a stochastic calculus with respect to the fractional Brownian motion.
David Nualart
Malliavin Calculus in finance
Abstract
In this chapter we review some applications of Malliavin Calculus to mathematical finance. First we discuss a probabilistic method for numerical computations of price sensitivities (Greeks) based on the integration by parts formula. Then, we discuss the use of Clark-Ocone formula to find hedging portfolios in the Black-Scholes model. Finally, the last section deals with the computation of additional expected utility for insider traders.
David Nualart
6. Malliavin Calculus in finance
Backmatter
Metadaten
Titel
The Malliavin Calculus and Related Topics
verfasst von
David Nualart
Copyright-Jahr
2006
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-28329-4
Print ISBN
978-3-540-28328-7
DOI
https://doi.org/10.1007/3-540-28329-3