Product Description

Semimartingale Theory and Stochastic Calculus presents a systematic and detailed account of the general theory of stochastic processes, the semimartingale theory, and related stochastic calculus. The book emphasizes stochastic integration for semimartingales, characteristics of semimartingales, predictable representation properties and weak convergence of semimartingales. It also includes a concise treatment of absolute continuity and singularity, contiguity, and entire separation of measures by semimartingale approach. Two basic types of processes frequently encountered in applied probability and statistics are highlighted: processes with independent increments and marked point processes encountered frequently in applied probability and statistics. Semimartingale Theory and Stochastic Calculus is a self-contained and comprehensive book that will be valuable for research mathematicians, statisticians, engineers, and students.

Product Details

• Amazon Sales Rank: #1066076 in Books
• Published on: 1992-09-14
• Number of items: 1
• Binding: Hardcover
• 400 pages

Customer Reviews

not that readable, a good reference
I read the previous review and borrowed this book from library. I am not from math/statistics, but I followed the construction of the stochastic integrals with respect to continuous semimartingales (from 'Diffusions, Markov Processes and Martingales') without difficult (well, somewhat, but not major). The thing is I found this book is not that easy to read, especially the part for general processes, i.e. predictable times, accessible times, predictable projections, etc. By browsing the book, I can only grasp the results they derive, but can't follow the details because of the heavy machinery. Nevertheless, this books contains full details, compared to 'Limit Theorems for Stochastic Processes' (I haven't read all, neither have I read C. Dellacherie and P. A. Meyer), and it suffices to be a good reference book.

a readable book on martingale theory of Strasbourg school
The main contents of this book can be found in other more famous monographs: Probabilities and Potential, Vol. 1 and 2, by Dellacherie/Meyer; Limit Theorems for Stochastic Processes, by Jacod/Shiryaev. Unfortunately, these monographs are very hard to read. So the main merit of He-Wang-Yan can be said: it's suitable for self-study and accessible to any persistent graduate student with certain mathematical maturity.
1. It contains the essence of Dellacherie/Meyer, but debugs/simplifies many proofs.
2. It gives a quick and clear presentation of Dellacherie's capacity theory, with application to section theorems.
3. This book is supplemented and further developed in the form of
problems. Some of the problems are useful results and some of them are difficult.

Contents: 1. Preliminaries. 2. Classical martingale theory. 3. Processes and stopping times. 4. Section theorems and their applications. 5. Projections of processes. 6. Martingales with integrable variation and square integrable martingales. 7. Local martingales. 8. Semimartingales and quasimartingales. 9. Stochastic integrals. 10. Martingale spaces H1 and BMO. 11. The characteristics of semimartingales. 12. Changes of measures. 13. Predictable representation property. 14. Absolute continuity and contiguity of measures. 15.Weak convergence for cadlag processes. 16.Weak convergence for semimartingales.

The first ten chapters (about 400 pages) are pretty easy to read. From Chapter 11 on, things get dramatically complicated as the authors try to work under the most general framework. For Girsanov's theorem, local absolute continuity is considered, and Jacod's random measures become the common language. These stuffs seem too complicated to a beginner. Other books should be consulted to see the most useful forms of these theorems. For example, I'm more happy with Protter's presentation of PRP (predictable presentation property), in the second edition of his book on SDE.

As to the last three chapters, I didn't really read them, but chapter 15 seems very nice and quite self-contained.

For the follow-up reading of He-Wang-Yan, I would recommend Revuz/Yor: Continuous Martingales and Brownian Motion, and Vol.2 of Rogers/Williams: Diffusions, Martingales and Markov Processes. They will show the applications, as well as intuitions, which are often obscured by the heavy machineries invented to takcle the most general cases.

A final remark: Frank Knight gave an interesting review of the book by Revuz/Yor, which is worth of looking. The review can be accessed at MathSciNet.