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Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics) [Print Replica] [Kindle Edition]

David Ruppert
3.5 out of 5 stars  See all reviews (18 customer reviews)

Print List Price: $99.00
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  • Print ISBN-10: 1441977864
  • Print ISBN-13: 978-1441977861
  • Edition: 2011
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Book Description

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.Some exposure to finance is helpful.


Editorial Reviews

Review

From the reviews:

“Book under review is aimed at Master’s students in a financial engineering program and spans the gap between some very basic finance concepts and some very advanced statistical concepts … . The book is evidently intended as, and is best approached as, a kind of working text, giving students the opportunity to work in detail through a variety of examples. The substantial chapters on regression and time series are particularly helpful in this regard. There is lots of useful R code and many example analyses.” (R. A. Maller, Mathematical Reviews, Issue 2012 d)

From the Back Cover

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.

David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semiparametric regression, functional data analysis, biostatistics, model calibration, measurement error, and astrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics's Lecture Notes--Monographs Series, and former Associate Editor of several major statistics journals. Professor Ruppert has published over 100 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.


Product Details

  • File Size: 15704 KB
  • Print Length: 658 pages
  • Publisher: Springer; 2011 edition (September 10, 2013)
  • Sold by: Amazon Digital Services, Inc.
  • Language: English
  • ASIN: B00BMTJ73Y
  • Text-to-Speech: Not enabled
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  • Amazon Best Sellers Rank: #604,399 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

Most Helpful Customer Reviews
21 of 22 people found the following review helpful
5.0 out of 5 stars Exellent book for those seeking professionalism. January 10, 2012
Format:Hardcover|Verified Purchase
This book is designed for those who want to apply data analysis in finance full force. The beauty of the book is in its in-depth coverage of data analysis through application of R. Many other books on financial engineering leave software implementation out of equation. In this book, R and financial engineering are interwoven with each other, which makes the book extremely practical and allows the reader to develop professional and real-world techniques while working through the text and completing exercises. If you are disciplined enough to work through the book and to complete all the exercises, you will save yourself tens of thousands of dollars, which is the cost of professional computational finance programs which cover similar topics. Just keep in mind as well that the book does not cover derivatives and focuses on statistical part of finance. There are numerous excellent texts for derivatives which would be recommended in addition to the book under review (for example, Hull, Joshi). In order to enjoy the journey through the book, you need to have some background though: you need to be fluent in high-level statistics and programming and if you don't know R yet, you will need to go though it first.
If you want to be an accomplished professional in mathematical and computational finance, and if you start entering mathematical finance from a different quantitative field, I definitely recommend to go through this book, in addition to Hull and Joshi texts on mathematical finance and C++.
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28 of 31 people found the following review helpful
4.0 out of 5 stars It keeps getting better all the time August 1, 2011
Format:Hardcover|Verified Purchase
I have had this book assigned for two courses in a computational finance master's degree program. I have read almost every chapter in the book at least once. In some cases I have poured over the chapters in order to solve the end-of-chapter problems.

Ruppert provides sparse coverage for complex material. He intends this book for readers who already know most of the math, but just want to know how the math they already know can be applied in finance. I have had professors who augmented the book with lecture notes. If I had to depend on this book alone to learn the material, I'd be in trouble.

This said, the chapters vary. Some are very good and some leave very little behind after I've read them. The chapters that are the best are those where the author seems to assume less knowledge on the part of the reader. The chapter on the Capital Assent Pricing model was surprisingly readable and clear. Oddly, the chapter on portfolio construction is not very good.

All of this is too bad, since the author clearly poured a great deal of time and effort into this book. No other finance book I've seen covers such a wide range of material. Perhaps the author felt that adding more introductory material would have made the book impossibly long. In depth coverage of many of the topics covered in this book (time series analysis, portfolio construction, univariate and multivariate statistics...) would take a book shelf of books.

Although this books has its problems, I have found that it can also be an important reference to have on my bookshelf. I recently found the chapters on linear regression and linear regression theory very useful.

After the first class in which I used this book, I was not very happy with it.
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10 of 11 people found the following review helpful
Format:Hardcover|Verified Purchase
Let me start with the good. The book is well written and informationally dense, which I like. He also writes with a terse, matter of fact style that is effective in communicating complex subject matter.

Working through the book however, I would have to say that I am/was incredibly disappointed that there was not a solutions manual or answer key provided to help those of us who are not advanced R users or course instructors. I am sure I am not the only person who learns best by attempting to solve the problem, then reviewing the correct answer, and then working backwards to find my errors and see where my logic was flawed or could be improved. To me this helps develop problem solving intuition.

Most textbooks provide solution sets to at least some of the problems. This is my biggest issue with the book and the only reason I can't rate it any higher. If anyone knows of a forum where people work on the problems together I would appreciate the recommendation!
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20 of 24 people found the following review helpful
1.0 out of 5 stars Kindle formulas are unreadable July 13, 2013
Format:Hardcover|Verified Purchase
The kindle version is useless. The equations are unreadable. Very disappointing. I wish we could trade in our kindle versions for a discount on paper versions.
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4 of 4 people found the following review helpful
2.0 out of 5 stars Where is the update to the data sets in R? December 30, 2013
Format:Hardcover|Verified Purchase
The book uses fEcofin for many of the R sections, but that package is not available for version 3.01. Makes most of the R stuff much less useful.
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5 of 7 people found the following review helpful
4.0 out of 5 stars Very comprehensive book. Not for stats novices April 1, 2013
By Tristan
Format:Hardcover|Verified Purchase
If you've got a good handle on statistics, and want to apply it to financial data, this is an excellent guide and reference resource.
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8 of 12 people found the following review helpful
1.0 out of 5 stars Kindle version is unusable... July 7, 2013
Format:Kindle Edition|Verified Purchase
There is a problem with Kindle version... While it was supposed to look just like a real book, this book looks just like a real book EXCEPT that all formulas are unreadable - you get random chapters instead of formulas!!!

At least on Kindle for iPad mini. Too bad, price is right, but not if you can't read the book.
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4.0 out of 5 stars For well-prepared readers only August 19, 2013
Format:Paperback|Verified Purchase
This is an aptly titled and excellent book on advanced treatment of financial data. Making use of it requires a good working knowledge of undergraduate statistics plus experience in programming. The book's examples and problems are based on the "R" language, which is not hard to learn IF you are an experienced programmer in at least one other language or even if you have mastered advanced use of Excel spreadsheets. You will be slowed down, as I am, by learning "R" as you go, and you will need to purchase and study at least one other book on the language. (I suggest "R for Dummies" as a start.) If, however, you are not familiar with statistical hypothesis testing, regression, and use of distributions, you will be at a complete loss.
As a retired engineer and long-time individual investor I find that most of the material is of little use to me directly but it is an intellectual treat and a mental challenge. Full employment of the book's content would be required only of a "quant" in a large hedge fund or financial/academic institution. In order to select stocks I don't need to know how to find the most likely distribution to fit historic stock price data.Time spent beating a dead horse is better invested searching the corral for a new one. For me, the useful sections are those on portfolio theory, regression and its aspects, simulation,and the "CAPM".
I would have given the book five stars if it were not so full of typos. I wasted more than an hour going through the book with the Errata from the web site, finding and correcting these errors. I have the 2010 edition and there is a later edition, possibly with fewer errors. Another annoying matter is the lack of answers to at least some of the problems.
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Most Recent Customer Reviews
1.0 out of 5 stars Poor digital version
Not available on iPad, and the translation to iPhone is extremely poor, as certain symbols are modified rendering many formulas incomprehensible. Read more
Published 13 days ago by adam kirk
5.0 out of 5 stars Very useful if you are a Financial Engineer student
Concisely covers many topics with R problems (best part of the book). Very useful if you are a Financial Engineer student. Read more
Published 1 month ago by Jetnor Murataj
5.0 out of 5 stars An excellent overview of existing theory and models which are applied...
An excellent overview of existing theory and models which are applied to financial data. It doesn't dive into details greatly however a lot of references are pointed out for deeper... Read more
Published 4 months ago by Pavel
3.0 out of 5 stars Great Book if only had answers to chapter questions
Very good book but lack of answers to the questions makes it very difficult to study by yourself. I do not understand the logic as to why you cannot purchase the solutions manual... Read more
Published 9 months ago by Jason
4.0 out of 5 stars Great book
The book is amazing for students of financial engineering. There are many examples and exercises, references in each chapters. I recommend.
Published 11 months ago by Vinicius
1.0 out of 5 stars Have to agree: Kindle version unusable
I don't know how the people that create the Kindle versions of books operate, but nobody could have looked at this book on a Kindle without realizing it's fundamentally unusable. Read more
Published 18 months ago by Michael D. Colacino
5.0 out of 5 stars Good intro
How appropriate are the models used in financial engineering? Answer depends on implicit assumptions behind the specific model used, from various angles including economic... Read more
Published 21 months ago by Administrator
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