With this book you'll impress a potential employer how deep your knowledge of stochastic calculus is. The book has proposed problems with some hints for the solutions. Solving the problems will make you an SDE guru.

If you aren't a bit of a Math wonk, this book can be a bit daunting. But it is worth wading through the Math if you want to understand the "WHY" behind all those formulas and results. If you are looking for a gentler introduction and the "real formulas" Quants use, check out Paul Wilmott's books.

The text generally starts with an intuitive example for the chapter and then starts methodically working through the underlying mathematics to get to the meaty results. The exercises are worth the effort as they reinforce the chapter work and offer additional insights.

Oksendal is not as formal as KS, Karatzas and Shreve (Brownian Motion and Stochastic Calculus), but it is easier to follow. The exercises in the first five chapters are very informative. Exercises in last chapters are more difficult (as they should be). After studying by this book, you may want to go deeper by using KS.

I read this book after I had read Karatzas' and Shreve's book "Stochastic Calculus..." and it is probably better to do it the other way round. The mathematical prerequisites are not high, however a good intuitive understanding of measure theory is probably necessary. The pace of the book is leasurely, the proofs are such, that pencil and paper is rarely needed, however no rigor is lost.
The book quickly moves to interesting applications of the theory, which is motivated very well.
It contains a few typographical errors, mostly in the last chapter, and mostly of a harmless nature.

With the necessary mathematical background, it seems to be an ideal introduction to this highly interesting topic of stochastic differential equations!

A well written book in Mathematics
Stochastic Differential Equations is a branch of mathematics. This book is not just for financial derivatives analysis or modeling. Oksendal first introduces the subject by raising a few stochastic problems (population growth; electric charge in RLC circuit; filtering problems, Dirichlet problems; asset management; optimal portfolio and options pricing) in the first chapter. The subsequent chapters develop notions and techniques which are able to solve wide varieties of stochastic problems (not just those mentioned in the first chapters). The arrangement is impressive in particular for readers who have no previous knowledge about the subject. The readers at least know the target for developing the techniques and would not lose the way when manipulating tons of symbols. Hints and answers to selected problems are invaluable to students for self-study.
To achieve a sound background on stochastic equations is extremely important especially in quantitative finance. It is not an easy job however. QF students may consider going through this book before seriously take Shreve's books on Stochastic Calculus for Finance.

This is a standard work (it is the one I read when I first started looking at this sort of thing) but having taken it off the shelf recently again, I think it is overrated, for several reasons.

First, it is very notation heavy - TeX has seduced Mr. Oksendahl into all sorts of bad habits - I can very easily imagine that the earlier editions (mine is the 5th), which were written with a typewriter, are much more readable.

Second, the proofs are very formal, developed mostly in terms of classical functional analysis (square integrable real functions, geometry of real Hilbert spaces etc.). From the point of view of rigor this is fine, but from the point of view of intuition, not so much, esp. when combined with the heavyweight notation. In fact note that unless you have a decent background in functional analysis, of the sort you are more likely to pick up in a mathematics degree than a finance degree, then you are going to get precisely nowhere with this book.

I don't want to be too negative, and there is lots of good stuff here - just to warn that Oksendahl is not (as one might think) a royal road to the theory of SDEs (depressingly, it may be that Oksendahl is, nevertheless, the best of the bunch out there - it is certainly, all criticism not-withstanding, more accessible than Karatzas and Shreve).

Very good book. However the math prerequisite is at quite a high level. Especially the probability theory introduction could be a little less fast-paced. As my main interest was on the financial application I would not have minded a little more on that topic and a little less on e.g. filtering or stochatsic control in return.

Very good book. However the math prerequisite is at quite a high level. Especially the probability theory introduction could be a little less fast-paced. As my main interest was on the financial application I would not have minded a little more on that topic and a little less on e.g. filtering or stochatsic control in return.

This is a good intro book. It brings you really fast to Ito's Formula and SDE. Somebody said that the Kolmogorov's backward eq in chapter 8 is wrong. This is false, it is just expressed in a different way than the usual form. However, with a change of time, you are all set.

The exposition is correct and concise, but too dense for someone without an extensive mathematical background.

I would much rather recommend Shreve's Stochastic Calculus for Finance II. Though longer, it is much more well-motivated and gives you a more intuitive feel for the concepts as opposed to Oksendal's full-on theoreical treatment.

From the cover, one can infer that this book means business. Some books still try to be artistic to attract audiences, whereas this book does away with a creative cover altogether. How often do you see that a book's cover contains five sample paths of a geometric Brownian Motion? Inside, Oksendal writes very clearly and uses the same format throughout. Although the topic is not the easiest to understand, you can acquire the skills that would allow you to gain sufficient knowledge of stochastic differential equations. He starts off with a good introduction and then moves on to the main topics. His applications to finance are also very useful for those in the field. A word of caution is that you would need a decent background in mathematics to read this book, but it is easier than Shreve or Karatzas and Shreve.

The text is reasonably acessible and presents the topics in an adequate sequence, with theorems/lemmas and respective proofs followed by solved and proposed exercises wich are part of the learning process. I recommend this book.

This my recommendation for people who want to learn stochastic calculus for the first time. The virtue of this book is that it keeps matters simple,well grounded, and intuitive enough to hook the newcomers in the subject. Once you get comfortable enough and want to learn technical detail necessary for scholarly research, there are other excellent expositions such as Karatzas and Shreve(1998) and Protter(1990). Some reviews complained that this book is limited to stochastic integration with respect to Brownian motion, but that is precisely why I recommend this book. By starting with Browning motion readers can form concrete mental image of stochastic integration and get ready to stride to more general setting if necessary.
Another virtue of this book is the plenty (easy) exercise problems. Working through them is perhaps the best way to learn stochastic calculus.

Oksendal develops stochastic integration, but only integration with respect to a Brownian Motion integrator, not with respect to local martingale integrators (e.g. The Ito process). This presents a problem because in his Finance section (and question 4.3) he wishes to integrate with respect to an Ito process, which is not neccessarily a Brownian Motion. Based on the theory he has developed in the book, this is not justified!!

It's actually a very good book if you need to learn the topic quickly, armed with a good background in probability theory you will have no difficulty getting through the first 1/3 of the book and gain a working knowledge of SDEs, Ito calculus etc. IT is at times concise in the sense that it lacks motivation etc., but the exposition is such that this presents no major hurdles, as the proofs are clear and short, there are very few errors, except the ones mentioned by the reviewer below, which I should double-check again because I didn't really use this book for its feynman-kac formula (there are better books out there for that). An excellent feature of the book, for those wanting examples from physics and other applied fields, are the problems at the end of chapters. You should definitely give it a try, many of them present the necessary motivation (solutions are at the end of the book). Despite the criticism below, which I consider minor (i.e. it could easily be fixed in a subsequent edition), it is a standard textbook for SDEs, which many respectable mathematicians recommend. Books should be judged by how many times they are quoted by experts, and this book certainly has been cited many times.

His version of Kolmogorov backward equation(Theorem 8.1.1, p. 139) is just wrong. Of course, then his Feynman-Kac formula(Theorem 8.2.1, p. 143) is wrong as well.
The correct versions can be found in numerous other books, like
Karatzas and Shreve, Shreve II(2004), Gikhman and Skorokhod, and on and on.

If calculus is to real analysis then this book is an attempt at filling in _____ is to stochastic analysis. Stochastic analysis is a difficult topic and a simplified introduction with minimal prerequisites is a great goal. However, this book has not fullfilled its promise.

There are a number of complaints to be made about this book. Most importantly is that in his attempt at simplification, Oksendal frequently chooses shedding (important) details over properly motivating a new concept. I found this particularly true in his exposition of generators. The book is poorly also organized: a number of topics are arbitrarily split into different chapters, important ideas hide inside of examples, etc.

While this is not my favorite book by any means, there is currently no replacement for it. Jumping directly into a book like Karatzas&Shreeve can be daunting. I would recommend getting a used copy. Also, previous editions seem to be very nearly identical to the current edition.

I also recommend checking out Rogers&Williams "Diffusions, Markov Process, and Martingales" Vols I&II.

When I became a quant, I needed to learn stochastic calculus and stochastic differential equations. Luckily, I found this book, which covers a lot of difficult concepts in a rigorous but accessible way. Oksendal is an excellent writer: his proofs are very clear (and usually not too terse), he provides very illustrative examples, and he does a great job of anticipating where the reader might get stuck. In addition, the problems at the end of the chapters do a good job of reinforcing central theorems and ideas. After reading this book, you'll be able to read most of the academic financial literature and all finance textbooks.

I've read lots of math books, and this is undoubtedly the best one I've ever seen. The only necessary backround is a solid understanding of measure theory.

This book is excellent if you already know why you want to know the material in it. Then it is concise, to the point, and very well-written. I turn back to it over and over again; my copy is very worn by now.

When I first started reading it, I was not too pleased with it. As a text-book it suffers from not motivating the theory, and not connecting it with parallel approaches. The subtitle mentions applications. Now, what one person considers applications is what the next person considers abstractions. My point of view is truly applied - I want to use SDE's to model real-world phenomena (actually, not financial ones) and are less interested in SDE's per se. So I would have liked more connections with physics (for instance advection-diffusion transport phenomena) and I would have liked the material to be more solidly anchored in general stochastic processes. Nevertheless, I appreciate that the book wouldn't have been as concise, then.

This a perfectly written book on stochastic calculus, especially needed for junior (but rising!) financial quants. All themes are carried out with a profound pedagogical talent. For a practitioner, the book loses nothing to Karatsas and Shreve, but is a much shorter, simpler and joyable reading. Yet, it is a systematic text book that covers most classical results with (important!) accessible proofs. For example, the Kolmogorov equations (forward and backward) are derived, not just stated as in most other texts, Girsanov's theorem is relatively well covered (although the author has not demonstrated its computational side well enough, but this is a common disease). Ideas are illustrated by practical problems (including those from quantitative finance). What I also liked, Oksendal's SDE theory is much closer to "differential equations", than what is often presented by probabilists. A must for every practitioner who works with stochatic processes.

As a so-called practicising 'quant' in a top Wall Street Investment bank, I came upon this book from colleagues who raved about the exposition of the material found in this book.Indeed even though I have an engineering and advanced mathematical background, I find the material to be useful in helping to understand the more research oriented finance journals. From a practitioner's view point the most fundamental aspect of the book is the statement where it states the solutions of SDE's can be thought of as inherent Browian motions for it is the latter which enables one to price the financial instruments one commonly hears of in the press.I would recomend first understanding the physical and mathematical aspects of Browninan motion before tackling the abstract field of stochastic calculus so that meaningful interpretations can be drawn.Nevertheless the book gives excellent penetrating coverage of what SDE's are.For budding so-called 'rocket-scientists' who want to make mega-bucks on the markets, my advice would be to first master partial differential equations (because this is the only way to pragmatically price things like exotic derivatives) then for their own enlightenment they can read this book if only just to keep up with the jones's.

This is a book I recommend as a TA in a mathematical finance Masters program. It gives a mathematically rigorous presentation of Stocastic Differential Equations without getting bogged down in too much detail, as do many books from a probability/stochastic processes background. It also illustrates the beautiful connection between SDEs and the heat equation. I recommend this book to anyone trying to read Karakas and Shreve for the first time.

This book is very easy to follow through the basics of stochastic calculus. The writing and examples remain fluid throughout the book, but the more difficult material could use a bit more in the way of examples.