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2005 | Buch | 2. Auflage

Stochastic Integration and Differential Equations

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Über dieses Buch

It has been 15 years since the first edition of Stochastic Integration and Differential Equations, A New Approach appeared, and in those years many other texts on the same subject have been published, often with connections to applications, especially mathematical finance. Yet in spite of the apparent simplicity of approach, none of these books has used the functional analytic method of presenting semimartingales and stochastic integration. Thus a 2nd edition seems worthwhile and timely, though it is no longer appropriate to call it "a new approach".

The new edition has several significant changes, most prominently the addition of exercises for solution. These are intended to supplement the text, but lemmas needed in a proof are never relegated to the exercises. Many of the exercises have been tested by graduate students at Purdue and Cornell Universities. Chapter 3 has been completely redone, with a new, more intuitive and simultaneously elementary proof of the fundamental Doob-Meyer decomposition theorem, the more general version of the Girsanov theorem due to Lenglart, the Kazamaki-Novikov criteria for exponential local martingales to be martingales, and a modern treatment of compensators. Chapter 4 treats sigma martingales (important in finance theory) and gives a more comprehensive treatment of martingale representation, including both the Jacod-Yor theory and Emery’s examples of martingales that actually have martingale representation (thus going beyond the standard cases of Brownian motion and the compensated Poisson process). New topics added include an introduction to the theory of the expansion of filtrations, a treatment of the Fefferman martingale inequality, and that the dual space of the martingale space H^1 can be identified with BMO martingales. Solutions to selected exercises are available at the web site of the author, with current URL http://www.orie.cornell.edu/~protter/books.html.

Inhaltsverzeichnis

Frontmatter
Introduction
Abstract
In this book we present a new approach to the theory of modern stochastic integration. The novelty is that we define a semimartingale as a stochastic process which is a “good integrator” on an elementary class of processes, rather than as a process that can be written as the sum of a local martingale and an adapted process with paths of finite variation on compacts: This approach has the advantage over the customary approach of not requiring a close analysis of the structure of martingales as a prerequisite. This is a significant advantage because such an analysis of martingales itself requires a highly technical body of knowledge known as “the general theory of processes.” Our approach has a further advantage of giving traditionally difficult and non-intuitive theorems (such as Stricker’s Theorem) transparently simple proofs. We have tried to capitalize on the natural advantage of our approach by systematically choosing the simplest, least technical proofs and presentations. As an example we have used K. M. Rao’s proofs of the Doob-Meyer decomposition theorems in Chap. III, rather than the more abstract but less intuitive Doléans-Dade measure approach.
Philip E. Protter
I. Preliminaries
Abstract
We assume as given a complete probability space (Ω,F, P). In addition we are given a filtration (F t)0≤t≤∞. By a filtration we mean a family of σ-algebras (F t)0≤t≤∞ that is increasing, i.e., F sF t if st. For convenience, we will usually write F for the filtration (F t)0≤t≤∞.
Philip E. Protter
II. Semimartingales and Stochastic Integrals
Abstract
The purpose of the theory of stochastic integration is to give a reasonable meaning to the idea of a differential to as wide a class of stochastic processes as possible. We saw in Sect. 8 of Chap. I that using Stieltjes integration on a path-by-path basis excludes such fundamental processes as Brownian motion, and martingales in general. Markov processes also in general have paths of unbounded variation and are similarly excluded. Therefore we must find an approach more general than Stieltjes integration.
Philip E. Protter
III. Semimartingales and Decomposable Processes
Abstract
In Chap. II we defined a semimartingale as a good integrator and we developed a theory of stochastic integration for integrands in L, the space of adapted processes with left continuous, right-limited paths. Such a space of integrands suffices to establish a change of variables formula (or “Itô’s formula”), and it also suffices for many applications, such as the study of stochastic differential equations. Nevertheless the space L is not general enough for the consideration of such important topics as local times and martingale representation theorems. We need a space of integrands analogous to measurable functions in the theory of Lebesgue integration. Thus defining an integral as a limit of sums—which requires a degree of smoothness on the sample paths—is inadequate. In this chapter we lay the groundwork necessary for an extension of our space of integrands, and the stochastic integral is then extended in Chap. IV.
Philip E. Protter
IV. General Stochastic Integration and Local Times
Abstract
We defined a semimartingale as a “good integrator” in Chap. II, and this led naturally to defining the stochastic integral as a limit of sums. To express an integral as a limit of sums requires some path smoothness of the integrands and we limited our attention to processes in L, the space of adapted processes with paths that are left continuous and have right limits The space L is sufficient to prove Itô’s formula, the Girsanov-Meyer Theorem, and it also suffices in some applications such as stochastic differential equations. But other uses, such as martingale representation theory or local times, require a larger space of integrands.
Philip E. Protter
V. Stochastic Differential Equations
Abstract
A diffusion can be thought of as a strong Markov process (in ℝn) with continuous paths. Before the development of Itô’s theory of stochastic integration for Brownian motion, the primary method of studying diffusions was to study their transition semigroups. This was equivalent to studying the infinitesimal generators of their semigroups, which are partial differential operators. Thus Feller’s investigations of diffusions (for example) were actually investigations of partial differential equations, inspired by diffusions.
Philip E. Protter
VI. Expansion of Filtrations
Abstract
By an expansion of the filtration F = (ℱt)t≥0, we mean that we expand the filtration F to get a new filtration ℍ=(H t)t≥0 which satisfies the usual hypotheses and F tH t each t ≥ 0. There are three questions we wish to address: (1) when does a specific, given semimartingale remain a semi-martingale in the enlarged filtration; (2) when do all semimartingales remain semimartingales in the enlarged filtration; (3) what is a new decomposition of the semimartingale for the new filtration.
Philip E. Protter
Backmatter
Metadaten
Titel
Stochastic Integration and Differential Equations
verfasst von
Philip E. Protter
Copyright-Jahr
2005
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-10061-5
Print ISBN
978-3-642-05560-7
DOI
https://doi.org/10.1007/978-3-662-10061-5