Tools for Computational Finance (Universitext)
| Author | : | |
| Rating | : | 4.39 (694 Votes) |
| Asin | : | B000QCQUOM |
| Format Type | : | |
| Number of Pages | : | 175 Pages |
| Publish Date | : | 2015-05-28 |
| Language | : | English |
DESCRIPTION:
It is an intermediate level text with an extremely practical focusThis is the kind of book you can read quickly, gaining a broad understanding of practical techniques of financial engineering. In an increasingly crowded field of financial engineering titles, Seydel's Tools for Computational Finance stands out as filling an unmet need. On the other hand, you can go through it slowly, working through all the examples and exercises in order to gain indepth practical knowledge you can use on the job.
Worth reading, but A Customer Seydel is at a level of sophistication comparable to Wilmott et al. (2000). Indeed, it makes a lot of sense to read both books side-by-side. While Wilmott focuses exclusively on "differential equation methods" of financial engineering, Seidel takes a more balanced approach. The two books complement each other well.The main part of this book is focused on methods of how to value ame. Yuanyuan Huang said the shortcut for rocket scientists.. During my painful thesis writting, I read this book in order to get some relaxation together with Hull's book. If you are a rocket scientist, such as a struggeling ph.d candidata like me, you will find Hull's book is useful but simplified. This book is quite helpful to me because I used PDE. Statistic, SDE, numerical method here and there and in the book, Seydel shows how to put th. "Update abt this book !" according to B. Jayapal. I was recommended couple of books for my course and this was one among them. Though it touches the topic, the contents are brief and if you want more detail, this is not the right one.
"riskbook "Remarkably, Seydel addresses students of both mathematics and business, presumes only minimal background in either subject, yet ventures deep into the subject in little more than 200 pages. Shreve (1998), which presume research-level preparation in probability theory, delve deeper into theoretical issues, but ignore numerics. Various methods are introduced from a problem-solving point of view, been eventually formulated and summarised as algorithms which are offered for straightforward implementation in computer programmes. On the other hand, you can go through it slowly, working through all the examples and exercises in order to gain indepth practical knowledge you can use on the job. This expository style, which is similar to Kloeden´s and Platen´s 'Numerical Solutions of SDEs through computer