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Stochastic Differential Equations: An Introduction With Applications (Anglais) Broché – 30 juin 1998

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4,5 étoiles sur 5 6 commentaires provenant des USA

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Book by Bernt K Oksendal

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Amazon.com: 4.5 étoiles sur 5 6 commentaires
4.0 étoiles sur 5 The Bible 27 février 2014
Par Bachelier - Publié sur Amazon.com
Format: Broché Achat vérifié
Stochastic Differential Equations have wide applications, from water treatment to earthquake prediction and measurement to weather prediction, but the place where they have been most thoroughly articulated is where the money is, and that is finance.

Oksendal uses finance examples a lot, but also makes "general" cases a lot so that you understand the wider implications of those spelled out here.

On notation: probably the best thing that can be said about Oksendal is he has no notational idiosyncrasies that make reading the work tough sledding (or reading in Chinese!). It is fair to say that between pure mathematicians, engineers, finance-types, and other sources of muddied water (compare Leibniz and Newton's notation for calculus, for example) approaching any work is always with the dread and trepidation "can I slog through the notation...I'm an [engineer, mathematician, options trader, etc.]?" The answer is: yes, but it is going to take some work, but no, it is not completely incomprehensible.

I sorta laugh at the subtitle "An Introduction[...]" because at over 500 mind-numbing pages and little to say beyond what is said here you'd really need to search out in some obscure journals to get beyond this. What I think he means is, "these are the base models...you engineers will but cement or water density here, you finance guys will put volatility here, etc."

This is a great companion piece for Shreve and Karatzas.
4.0 étoiles sur 5 Four Stars 15 février 2015
Par Aqeel S. Madhag - Publié sur Amazon.com
Format: Broché Achat vérifié
nice item, thanks
5.0 étoiles sur 5 Very good book 19 septembre 2008
Par H. Aliaga - Publié sur Amazon.com
Format: Broché Achat vérifié
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.
34 internautes sur 35 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Simple, but rigorous book 17 août 2000
Par Alex Levin - Publié sur Amazon.com
Format: Broché
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.
18 internautes sur 21 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Good reference - not so good text-book 12 janvier 2001
Par Un client - Publié sur Amazon.com
Format: Broché
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.
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