Engineering-risk Books

Very nice book with a lot of SAS code. It is very helpful for the statistician who wants to enter the business area.

In the Data Mining field, this book is the most clear and concise and well-organized book for many years. This book truly deserves to be called the Data Mining Cookbook because it appeals to everyone interested in the subject. In other words, her writing style appeals to both the non-statistician and the statistician. The theory is well explained for the general public. She gives the kind of details that allows anyone with a college education and who is determined to be able to do some of this analysis on their own or at least supervise someone who is doing it for them.

Good book for learning about the data mining techniques of logistic and linear regression. It helped highlight some good uses, and fortunately, I've recently had the opportunity to use it in my work.
However, I was a bit disappointed that the data preparation seemed very coding intensive. The author could have shown readers how to merge lookup tables of risk values onto customer datasets, rather than hard-coding each of the rules and values; or to use the SAS procedure for creating indicator variables, instead of writing the rules for each category.
Overall, I'm glad that I purchased the book - it lives up to its claims - but it misses some of the better practices, and time saving devices, in data preparation

This book is really a useful step by step guidance to build a model using logistic regression. It is very practical and to the point. This book covers the business envrionment from high level and go down to the working data level and then again relate how the results from mining the data can solve busines problem. It is a treasure for data mining analyst and modelers.
Just as the author point out, although there are many new model building techniques emerge every year, logistic regression still remains a very powerful data mining and model building tool. And it is well demonstrated in her detailed examples.

Logistic Regression From A - Z! This book has it all.

The author lays out clear, concise methodologies to build robust predictive models using SAS. The nice thing is this book lays out the process step by step with SAS code examples. You do not have to be a statistics major to understand how to use the built in SAS functionality.

The modeling methods are unbelievably detailed including topics like defining the objective function, testing variables for predictability using chi squared, fitting continuous variables using the most linear variable transformation format (squared, cubed, cubed root, log, exponent, tangent, sine, cosine, etc... 19 total formats), changing categorical variables to continuous indicator variables for logistic regression use, using stepwise, backward, and score regression methods to further eliminate less predictive variables, defining deciles, and model testing methods like bootstrapping, jackknifing and gains tables to validate the model.

I do not fully understand the mathematical concepts involved throughout the entire process nor do I want to. The book provides a consistent repeatable programming methodology to follow that is broken down into very quantifiable steps.

I would recommend this book for anyone with limited statistical knowledge and a need to understand predictive modeling programming methodologies. Knowledge of the SAS programming language is essential to make full use of this material. The book uses real life examples from the banking, insurance, and marketing industries and contains additional valuable information related to these fields.

I am an advocate of Agile Software Development methods so I read this book with that bias. What I found was this book quantifies many of the ideas of agile development that before just made intuitive sense. The book explains the potential value of developing in small steps, and has a great discussion of how incremental development and architecture interact to affect return on investment.

If you are not involved in the financial side of planning software projects you may find yourself skimming through parts, but the general themes are ones that everyone involved in planning software projects will find interesting. (This includes developers in small teams.)

In most cases customers want you to build a software application to help them make or save money, and reading this book will help you to understand that.

This book caught my attention because I was searching for a decent book on ROI and software development, and Dr. Jane Cleland-Huang was a professor at the graduate school I attended (I was never a student of hers and have never met her). Furthering my interest in "Software by Numbers" were the facts that it runs less than 200 pages and is from Sun Microsystems. In my opinion, most large technology works can be significantly shortened if the fluff is removed from them (who has time to read fluff?), and I view Sun as a respected firm that is involved with a lot of the technologies in my workplace. Maximizing financial return during the development of software is the main subject of this book, and the attempt of Jane and Mark Denne, her co-author, to show how to do so is a good one. Interestingly enough, the Preface of this text discusses how the authors arrived at the title "Software by Numbers", and although this title is better than the others that their friends and colleagues suggested, because it is a bit too similar to the common expression "paint by number" (which can be an idiomatic expression for "easy" or "mindless"), I do not think it does the book content justice. ROI is anything but a simple subject. Multiple formulas exist for ROI to begin with, and return can be a subjective matter because it can involve a lot of assumptions. Quite a few financial tables are presented with the book material, and a cursory review of the accompanying ROI examples shows that there is a bit of explanation missing. Of course, there is a tradeoff here between conciseness and thoroughness, and explaining all of the assumptions made would probably not be appropriate for most readers. The authors start the discussion by stating that software development is a very difficult endeavor. The following portions of the introductory chapters explain well much of the language used throughout the rest of the book, such as minimum marketable feature (MMF) and return on investment (ROI). The authors then compare MMF-based ROI with classic ROI and discuss how to discover MMFs, how to determine their value, and how this all fits with incremental funding methodology (IFM). While there exists overlap between the architecture discussion and the content of "Architecture in Practice" (see my review for that text), the authors quickly move forward to discussing the delivery of valued features to customers and how parallel development of multiple MMFs is often well suited for meeting customer needs. Although the chapter on how to manage intangibles provides a much more simple explanation than books such as "Making Technology Investments - ROI Road Map to Better Business Cases", the authors get their point across in a succinct manner. Similarly, the authors discuss how IFM can thrive when used with the (Rational) Unified Process and other more agile software engineering methodologies.

Software by Numbers focuses on the financial aspects of software development. It introduces a method called "Incremental Funding Method" which demonstrates how software development with incremental delivery can fund itself, therefore lowering investment costs and thus lowering the risks for starting the development. The key-idea in the method is probably to break up the feature sets of a software product in MMFs (minimum marketable features) and calculate the investment and ROI of these separately and then deciding in what order they could be developed to maximize the total ROI.

The ideas of software by numbers are important to understand when selecting a development method that enables incremental delivery (like most agile development methods). Its especially good in convincing people who do not know software development that incremental delivery is a very good idea and financially sound idea.

To me this book was definitively worth reading. However, at some points the book seemed to lose my attention. At points I felt the authors were just repeating the same points over and over again. Eventhough the book is not very thick, I felt it could have been even thinner and conveying the same message.

Still, it's a recommended read.

I read the reviews of this book and had high hopes. I needed a volume that helps me build business cases based on value propositions for management who knows little about software but a lot about business. Well this isn't the book. It is interesting. But, when you try to communicate value in terms that the business community doesn't understand, you fall flat on your face. For example, concepts like incremental funding methodology and minimum marketable features have lots of appeal to technies. But, they don't make sense when to a chief financial officer who doesn't have a deep software or the XP background. Perhaps, the book would work if I worked in a software house that built software using XP and the state-of-the-art. That would be nice. I guess I will search for another volume that is more traditional in its approach.

The core theory of the book is that feature/stories need to be prioritized based on business value. To anyone doing Agile (Scrum, XP, FDD) this is pretty much standard stuff.

What is new in the book is that they provide a framework for tying individual features to future cash flow/benefit for that feature to drive an analysis of net present value.

My viewpoint is that this book is very academic in orientation rather than grounded in the real world examples. As an example, there is no detailed example/sample/case study of how future cash flows were determined for a given set of features. The derivation of these values is critical to the applicability of the approach.

Do I buy it? No. I think XP User stories prioritized by the customer(s) will be a much more robust mechanism for selecting what to work on next. It will also come at a lower cost.

I found this book informative and extremely accessible. It's incredulous that all these dorks keep insisting that the title of this book rings false for them. The content does provide a great deal more than the title alone suggests but one could see that by glancing at the content prior to buying the book. What I enjoyed most about it was the matter of fact, easy tone which conveyed so much so simply.

This is a "How To Program" book which uses financial applications as samples. It is heavy on programming basics and scratches the surface of financial modeling. That's fine if that's what you expect from the book, but the title led me to believe that I'd learn modeling techniques. I expected a book that assumed proficiency in VB and dealt with moderate to advanced financial topics.

If you already know what a where clause is and are proficient at VB.Net, this is not for you. If you know nothing about VB and want to learn it using interesting examples, this is for you.

Lately I've been making heavy use of Amazon's Table of Content look up capability. I think that I didn't use that well enough when I purchased this book. More on that.

I like to buy books as reference material. Things should be easy to find. I bought this book when I was first getting into building Automated Trading Strategies. I had the impression that this would help me.

I no longer support that impression. The Table of Contents has no reflection whatsoever on the financial perspective of the book. If you are looking for inspiration on programming a particular financial capability, one is left with scanning the book sequentially from front to back. There is no detailed table of content. One learns very early in programming that random access may be a trifle harder, but in the end it is much faster. No random access here.

I get the impression the book includes a few financial snippets, but nothing that really builds on anything else. I could be wrong. My attention span was never long enough to persist in finding out stuff in the book.

On the bright side of things, if I was learning .VB and wanted to learn it in within the Financial Modelling world, it might be a good book to pick up.

But as other reviewers suggest, VB isn't a heavy hitter. I grew up with C/C++. Lately, I've been using C# substantially in my Financial Modelling.

So... I'd give this one two thumbs down due to it's lack of ability to be used as a reference. I'm sure there is good info in there somewhere, but how does one find it easily?

I'd instead pick up the O'Reilly book C# Essentials, your favorite Financial Book, and try things out that way.

The book is a jumble of basic financial concepts and way-too-simple VB.NET syntax explanations, which makes me wonder who are its target readers? How many financial professionals will ever want and/or be able to master .NET? As a computer programmer in a financial orgnization, I definitely want to learn a bit more about financial modelling, but this is certainly NOT the book to start with. It's far better to get familiarized with the basics by reading a pure finance or trading book.

Btw, if you read the job ads, there aren't many vacancies for trading system programmers to write VB6/VB.NET code (or any at all). Most would ask for a master in C/C++ or Java or some obscure proprietary language/platform.

Sub-Title: Using Visual Basic.NET and Databases to Create Pricing, Trading, and Risk Management Models

This book is written at the delicate interface between financial analysis and computer programming. You could say that it's a book on financial modelling that spends a lot of time telling you how to program. Perhaps more accurately, it is a book on computer programming but written with the vocabulary and direction necessary to have the computer programs automatically select and evaluate investment opportunities.

The particular programming techniques described here use the Microsoft .NET environment, so the examples are very Microsoft centric. Still much of the material would be easily transferred over to other development environments if the reader desires.

This book is not for the faint of heart - you should know statistics and have a very solid math background before you attempt to crack open the cover. While primarily focused on risk analysis within the finance and insurance industries, the principles outlined within can be carried across to other fields such as high-tech fraud. The probability calculations, Monte Carlo simulations, and other means of determining likelihood of an event occurring are all covered and will confuse the novice. Containing a slight bias towards Vose consulting services (where the author obviously hails), this book also points out what above-average risk consultation services should do for you, as well as what you, as a hiring manager, should expect.

All in all, I find this to be a great resource, and look forward to sharing the book with others in my department that are mathematicians and can truly appreciate the content.

A very good book, but a bit too much mathematical detail in deriving formulas for probability distributions; could use better descriptions of when to use each probability distribution.

Unlike in the first edition, the author seems to have tried his best to eliminate any reference to any simulation software in the second edition. Result: it now reads like any academic simulation text, only less. The first edition wasn't broke. Why fix it? Bring back the classic Vose!

This book gives a deep insight into the state of the art and recent developments of quantitative risk analysis using simulation methods. Describes topics such as second order risk analysis I never heard about before. I used the knowledge drawn from this book to write some technical papers (published on peer-reviewed journals and seminars proceedings). Specialized software, such as @-risk and crystal ball is not strictly needed to carry out the risk-analysis systems suggested by the author (but pretty advanced skills with excel or use of math softwares are required). The specific subject of the book is risk modelling by Monte Carlo Simulation and Bayesan analysis; it does not deal with fuzzy models or other uncertainty-propagation methods. I highly reccomend this book to anyone interested into the specific subject.

I believe that this book is the best of many Risk Analysis books. The book's structure, starting from fundamental topics and guiding to advanced topics, is excellent. So, I translated this book into Japanese! You will make the best use of the book with Excel add-in Monte Carlo simulation software like @Risk and Crystal ball that you can get its trial version from the vendor's site(free!). But, the value of this book is not decreased with its sophistitated notation even if you don't have such software. You can enjoy the logic of Quantitative Risk Analysis. Now, the author is preparing his original software. I hope it will be as valuable as this book.

The author goes right to the point. He explains well the VaR-related mathematics. There are a few mistakes, which would be easier to note if all derivations were provided. Overall, this is an excellent book.

When we went to implement a VaR system, the price tag was going to exceed seven figures. Needless to say, I didn't hesitate to drop some money buying the available books on VaR. They all say essentially the same things. For practical worked examples, you can't beat Butler. But unless you are an absolute beginner (do you know what delta and gamma are?) you may find it too basic. The all-round best book is Dowd. It is well organized and a pleasure to read. It covers the math, but without getting bogged down in meaningless derivations. For readers who want more information, there are plenty of references to original sources. I followed up on a number of these, and was pleasantly surprised at how easy some of this stuff is to assimilate.

Best book on VaR to date. Clear, concise, self-contained. Perfect combination of rigurous theory with practical applications. The author has done an excellent job at distilling what is relevant in Risk Management. This book is better than all the other VaR books written so far combined.

The author has done good work in introducing the basic concepts in Value-at-Risk. However, the text leaves some important statistical and implementation points hidden, making implementing VaR look far too easy. For example, there is no discussion about the problems involved in long-term forecasting of correlations and volatilities.

The much advertised "new distinctive investment approach", the so called "Generalized Sharpe Rule" is a rather naive treatment on classical risk/return analysis. However, the lack of mathematical rigour is well compensated with good references.

The author has done good work in introducing the basic concepts in Value-at-Risk. However, the text leaves some important statistical and implementation points hidden, making implementing VaR look far too easy. For example, there is no discussion about the problems involved in long-term forecasting of correlations and volatilities.

The much advertised "new distinctive investment approach", the so called "Generalized Sharpe Rule" is a rather naive treatment on classical risk/return analysis. However, the lack of mathematical rigour is well compensated with good references.

Mark's book actually teaches you how to properly use classes and inheritance (virtual functions) to implement derivative pricing models in C++. The slim book is actually quite 'thick' in the sense that you need to spend some time on understanding the design and ideas behind those codes. In addtion, the second ed. includes a chapter on XLW (a package links C++ to Excel and modified by MJ). After using XLW for some time now, I have to say that it is definitely one of the best applications for financial engineering.

This is the book to buy if you want to develop/improve object-oriented thinking in C++.

This slim volume is totally inadequate either as a C++ book or as a derivative pricing book. (After all, how much can you cover on either topic in just 170 pages or so?) I'm not sure what purpose it really serves. It seems to require the reader to know quite a bit of C++ yet things like virtual function should already be known by the reader. This is not good as either tutorial or reference.

This book is easy to read and hands on. Although there are a lot of excellent books which will give you an introduction to C++ for general purposes, I will still recommend this book to people who want to learn C++ for derivatives pricing.

Joshi's book is practical and concise. The whole book is project-based and through step-by-step method in his book, Joshi provide a vivid view towards how to construct a pricing engine in an object-oriented way. This book forces me to think in an object-oriented way and to think about code reusability, the logics and relation between different classes I put into my pricing engine. After my first semester's intense focus on this book, I found that my codes are just a nutshell of my whole logics and my understanding to the problem I want to solve, and then codes are extensible and readable.

It is not a book for reading, but rather a book for practicing. It is not an easy book. But some of my friends' interview questions are just the exercise in the book!! I would believe it is also a great book for preparing interviews.

Do not be put off by the above-average price/page-count multiple: it will take a lot of time and work to go through the book's 200 pages, and you won't regret the effort. This is not one's introduction to C++, nor is it a collection of ready-to-use code. Instead, the book sets out to demonstrate why you need OOP, and does that in the context of a single, progressively expanding, exercise.

Anyone can scribble a bunch of equations on paper and call it a book. Without sufficient context, they are useless.

Mathematics for Finance (An Introduction to Financial Engineering) is a book intended for undergrad students "IN MATHEMATICS" or other discipline with a relative high mathematical content.

The book assumes some basic notion of Calculus and Probability Theory and it is focused more on the mathematics than in its theory and application of Finance. If you are looking to dwell into the mathematics (Proof of Equations) this is a great book, but if you are looking for a book that is rich in theory and in application then you should consider "Option, Future and Other Derivatives" or "Quantitative Methods for Finance" as an alternative. Both books are "a most" for any finance student and are of great help. Now if you want an introduction into the mathematics behind Finance then this book is a perfect purchase.

Important to state that all the problems presented in this book are solved meaning that it is great for self teaching. Marek Capinsi and Thomas Zastawniak have done a great job on this book.

I gave it four stars, because it has room for impovement.

As a graduate student in Financial Engineering I have found this book useless.
The title of the book is "Mathematics for Finance", but can you find in it even an elementary introduction to the stochastic processes? No. Ditto for the Ito's lemma and many other topics. The derivation of the Black Scholes formula is just sketched, and the insight that you can get from it is very limited.

Nevertheless, I wouldn't mind these limitations if this book provided a clear introduction to more advanced topics: unfortunately this book is not good even in that. In comparison to other textbooks the theorems and definitions are convoluted and do not go straight to the point. For example, in Shreve's "Stochastic Calculus for Finance" or Baxter & Rennie "Financial Calculus" the Fundamental Theorem of Asset Pricing is stated in this way: "In a market with risk neutral probability there is no arbitrage". Can you find such a simple and explanatory definition in Capinski's book? Not at all. The theorem at page 83 (you can see it yourself by searching inside the book) basically says the same thing using 8 lines of text and little financial intuition.
The only good thing that I can say about this book is that all exercises are resolved.
Overall, "Mathematics for Finance" has been a big disappointment: it doesn't have either the mathematical depth of Shreve's books or the conciseness in explaining financial concepts of Baxter & Rennie.
Whatever is the level of education that you are pursuing, graduate or undergraduate, I don't see any point in using it.

I enjoyed reading the book and solving exercises in it. I have a Ph.D.in chemistry and my wife and I did our his and her's MBA in the 1990s. I wanted to learn more concepts in finance and needed an easy entry, something I could enjoy, and without spending much money. The book by Capinski came recommended from a friend who teaches Economics at Cal State. I can speak for myself: I feel reasonably informed and I feel the book gave me concepts I can use to handle my own portfolio.

In the future, this text should be offered with an interactive CD that contains Xls, matrix, calculus, and graphing capabilities so one (I) can visualize the outcomes of proposed solutions.

I can only echo the other reviewers. As far as I can tell this book has no serious competition. This is an excellent introduction to mathematical finance for those with a solid undergraduate level understanding of higher math but without graduate level exposure. I agree that it is ideal for self study as that is exactly what I am using it for. The price is right especially in contrast with its overpriced brethren. Five stars!

The major achievement of the book is concise presentation of the latest discoveries of the authors and their co-authors (Cont, Matacz). The discoveries are so significant that will lead in some 20 years to a Nobel Prize in Economics. They are: non-uniqueness of the option's price; role of kurtosis (the fourth moment of the price distribution) for volatility smile formula; a simple "square-root" formula for the FRC (forward rate curve of interest rate) accompanied by a simple explanation of a market mechanism behind it; deep "psychological" explanation (via Langevin equation) of the exponents 3-5 in the power-type tails of the price distributions; explanation of why VaR is systematically underestimated by Black-Scholes theory. However, all these discoveries require different mathematics and so far the authors are in search for the correct way to present them together coherently. There are several loose ends: many non-Gaussian approximations (which likely came from JPB's early works in physics and still beloved by him) without practical tools to estimate them; in the interesting chapter on random matrixes missing is a "market" explanation of the meaning of the eigenstates which stand behind 10% of "non-random" eigenvalues; absence of a serious discussion about exotic options points out to a difficulty to extend authors' methods toward more general options (while the regular PDE approach taken by other authors, like Wilmott, allows such an extension almost naturally).

Five stars for the intended audience, two stars for the likely holder (a theoretical approximation of the mathfin reader utility curve) give a three star average. Why? Practical utility skew is the operative third moment.

If you have no idea about what I just wrote, this book is not for you. If you do and it made you smile, keep reading.

In Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management authors Bouchaud and Potters place an additional veneer on their previous edition titled Theory of Financial Risks: From Statistical Physics to Risk Management, adding the sexy "Derivative Pricing" no doubt in a forgivable attempt to increase sales in this Googlfied world. But this is their failure. While the original edition was a fine, even respectable voice on bridging the knowledge of the intended audience of physicists-turned financial quant, this edition fails on the over covered subject of derivative pricing simply because it is not theoretical, but an empirical and technical review of historical data sets and assumptions and pricing techniques with critiques of the observed differences between theory and empirical results. Needless to say, this fails the smell test in physics, but in finance is as common as Shinola.

Sorry, but critiques of B-S assumptions and better curve fitting is technical, not theoretical. In other words, the theory of why third and fourth moments (skew and kurtosis) become operative and currently present arbitrage opportunities or risk management concerns is not adequately addressed, merely observed, expressed, and called attention to. Moreover, third and fourth moments are approached from a formulaic perspective intended primarily for risk managers and those seeking to make a buck (such as the authors themselves) and have only dangers emphasized. So formulas and expression yes, pure theory no.

Other reviewers have complained about a thematic Gauss-Levy versus Bachelier tone. Ho hum. For the day to day market maker (readers of Baird) such arguments pale in comparison to managing simply the delta of your book. For the physicist, the ghastly collection of noise and spikes that passes for a data set in finance will likely simply better be explained by long periods of madness followed by fleeting moments of clarity than any Procrustean attempt at better curve fitting informed for the empirical work of observing the data signals of a star's decay. Perhaps the only person Bouchaud and Potters's theoretical practical bridge tweaking would have assistance for would be the risk manager of the completely non-correlated short duration portion of the balance sheet of an international bank. Who also happened to be very powerful and have actual accurate real-time data and could implement these ideas. Scale? North of 8 billion before this is useful. Yep, in such a theta world Bachelier's technique rules. But we don't live in such a world yet, although risk managers everywhere delude themselves that they do, often armed with the likes of this book.

Let me hasten to add that Theory is not a bad thing, but its utility best serves the finmath community when it is clearly and explicitly so, without attempting techne and erte. This book is a forgivable beast with two backs, strongly skewed to a good critique of Theory and with fat tails of empiricism, and a bad attempt to be practical. This work therefore, again forgivably, is bound to disappoint practitioners. Joshi is your better bet.

Who is this book not for? Readers and users of Baird, Joshi and Hull and coding front-line quants and risk managers who live in a world of imperfect and delayed data sets will likely find this pointless academic obfuscation. Whom is this book for? I'm a finance guy, not a physicist, and so I read this book in a cyber book group with a theoretical physicist friend. He characterized the book as easy reading for him, but with little new to add that wasn't already known by the reasonably informed physicist turned finquant. His take was that it was a painfully obvious work, curiously passed off as original thinking when in reality it was simply a useful synthesis of common, though specialized knowledge. My take was it was tough sledding to get to obvious conclusions that anyone who has ever run an options book knows through painful experience or wise counsel. Elegantly expressed at a high level for a well-educated readership, but not exactly a holy grail. In other words, the juice wasn't worth the squeeze.

This book is a supposedly new approach to financial modeling from the viewpoint of "statistical physics". In fact, it is far from being that. First, there is little or no content really related to statistical physics in it. Apart from the fact that random variables and stochastic processes are also used in physics, the only feature in common between statistical physics and this book is some notational similarities and a lack of rigour which, justified in the case where it is supplemented by physical intuition, leads here to numerous mistakes and sloppy reasoning.

The title, while promising, is quite arrogant: not only there is no "theory of financial risks" in the book but many of the main issues of risk management are not even mentioned: Value at Risk receives less than a page at the end, while hedging of exotic options is not even an issue.

Also, while the first part of the book insists on choosing the correct distribution for price returns, the chapter on options exclusively gives computations for the case of ...Brownian motion (not even exponential Brownian motion)! One is left wondering whether these fancy models presented in the first part were worth mentioning?

Another point is the readership of this book: given the notational complexity of the book and the analogies with physics, only a PhD in theoretical physics can possibly find this book readable. In fact, a finance student will find it too light on the finance side while a math-minded student will find it too sloppy and imprecise.

The surprisingly low level of mathematical rigour - one confuses regularly "uncorrelated" with "independence"- is nevertheless accompanied by an incredibly sophisticated set of tools such as random matrix theory, which are exotic even for professional researchers. Perhaps it would be better to spend more time explaining the concept of stochastic volatility or nonstationarity than rocketing the reader into unknown grounds...

I come to the conclusion that the aim of the book is more to impress the reader about the technical sophistication of the authors than to teach anything in a clear manner.

Although OK as a bedtime reader, this book certainly does not contain anything one can practically implement: in fact the presentation is so imprecise that one is lost in the successive and uncontroled approximations, not knowing at the end what is the algorithm proposed to solve a given problem.

This text has a nice discussion of Levy distributions and (important!) discusses why the central limit theorem does not apply to the tails of a distribution in the limit of many independent random events. An exponential distribution is given as an example how the CLT fails. I was first happy to see a chapter devoted to portfolio selection, but the chapter (like most of the book) is very difficult to follow (I gave up on that chapter, unhappily, because it looked interesting). The notation could have been better (to be quite honest, the notation is horrible), and the arguments (many of which are original) could have been made sharper and clearer. For my taste, too many arguments in the text rely on uncontrolled approximations, with Gaussian results as special limiting cases. The chapters on options are original, introducing their idea of history-dependent strategies (however, to get a strategy other than the delta-hedge does not not require history-dependence, CAPM is an example), but the predictions too often go in the direction of showing how Gaussian returns can be retrieved in some limit (I find this the opposite of convincing!). For an introduction to options, the 1973 Black-Scholes paper is still the best (aside from the wrong claim that CAPM and the delta-hedge yield the same results). The argument in the introduction in favor of 'randomness' as the origin of macroscopic law left me as cold as a cucumber. On page 4 a density is called 'invariant' under change of variable whereas 'scalar' is the correct word (a common error in many texts on relativity). The explanation of Ito calculus is inventive but inadequate (see instead Baxter and Rennie for a correct and readable treatment, one the forms the basis for new research on local volatility). Also, utlility is once mentioned but never criticized. Had the book been more pedagogically written then one could well have used it as an introductory text, given the nice choice of topics discussed.

Unfortunately, but not surprisingly, the previous reviewer prefered to remain anonymous. Otherwise, we would happily have argued with him privately. But his review contains so many erroneous and obnoxious statements that we feel we have to reply publicly, at least on the most important points.

a) After spending a full chapter (2) on empirical data and faithful models to describe them, we only price options using...the Brownian motion, says our reviewer (not even the Black-Scholes model, adds he). Well, either the reviewer has only casually browsed through our book, or this is total bad faith and disinformation. After discussing a general option pricing formula, we indeed illustrate it first (4.3.3) with the Black-Scholes model, then with Bachelier's (Brownian) model which, as we explain, is actually a better model for short term options. But the rest of the chapter is entirely devoted to non-Gaussian effects: a theory of the smile, its relation with kurtosis and long-ranged correlation in the volatility, and comparison with actual market smiles (4.3.4), and more importantly, the hedging strategies and residual risk (4.4), alternative hedging strategies for Value-at-Risk control (4.4.6), etc. The emphasis on risk, absent in the Black-Scholes world, is our main message, and partly justifies the title of our book.

b) "There is no statistical physics" in our book, moans the reviewer. Our aim was not to draw phoney analogies, but to present this field in the spirit of statistical physics, with what we feel is an interesting balance between intuition and rigour. (Many physicists feel stranded when reading standard mathematical finance books, where data is scarce, and rigour hides the inadequacies of the models). However, there are several genuine inputs from statistical physics, e.g. data processing, approximations, simple agent based models (2.8-9), functional derivatives to obtain optimal hedges (4.4), saddle point estimates of the Value at Risk for complex portfolios (5.4) and finally, Random Matrices that the reviewer finds unduly complex -- perhaps only because new to him. However, this is contained in "starred" section, indicating that it can be skipped at first reading, as many more advanced sections.

Two more details. We indeed sometimes consider independent random variables, sometimes only uncorrelated, hopefully not confusing the two. If the reviewer spotted incorrect statements, we would be grateful to him if we can correct them in further editions. Second, our book is not meant to provide ready to implement recipes but to present a different way of thinking about finance. Nevertheless, many of the ideas have already been implemented and are used by several (open minded?) financial institutions.

The book does a fair job of giving an overview of emergency management. The veiw of FEMA is obviously written by persons wearing rose colored glasses. The glossing over of errors by FEMA and other emergency management agencies does not allow the reader to understand the evolution of emergency management. The book should be factual and less fluff.

I ordered a text book and got excellent service from Amazon. It got here in a timely fashion and in excellent condition.

An excellent introductory book with a good review of the basic components of emergency management

I found this to be a good introduction to emergency management. I am using it in my course on Disaster Preparidness: medical consequency management. Well written and easy to read for the novice.

This book is more useful for someone in government. If you need emergency planning/mangement for a business or industry, the book is a good starter but will be limited. Too bad it was published right before Katrina.

When this book was first assigned for a Novels and Ethnography course, I was less than enthused. Something about it, on a superficial level, did not instantly connect with my interest. However, upon getting to only the second page, I realized this work was something special. Gottlieb and Graham, an anthropologist and writer (married) share with readers their experience in Africa's Cote de Ivoire. They travel there together to live among the Beng and to study their traditional culture, but come away with much, much more. At first shunned for their western peculiarities, Alma and Philip are finally accepted and adopted into the village life. They stay for 15 months before returning to the U.S. The end of the book also shares a return to Kosangbe (their village of choice) after having spent five years at home missing the community they left behind.
This work is a fabulous look at the ways in which cultures interact and the ways in which truths can easily (and usually unintentionally) become fictions. Through this work, it is easy to see that cultures, which at first seem starkly contrasted, are not so different after all.
While it covers real research done by Gottlieb for her doctoral dissertation, the entire work reads like a very intricately involved novel. It is nothing if not "user friendly." Each chapter is alternated between wife (anthropologist) and husband (writer). Interestingly enough, their voices are very similar, yet with different concerns and nuances.
If you have enjoyed Kingsolver's Poisonwood Bible and/or Conrad's Heart of Darkness, you will certainly enjoy this work.

For anyone interested in cultural studies, or simply a fantastic, engaging read, this book is a must. I could not recommend it more highly.

I've used this book in two classes on the anthropology of religion, and the students love it. The anthropologist wife writes in a way they're familiar with from other ethnographies, and the husband in lovely prose. I think students react so well because the authors aren't afraid to write about their screw-ups, defeats, and fears. I use this as the first book in the semester, BEFORE I send students out into the field. It lets them know that they can do this scary thing called fieldwork and still be themselves.

Of course, I read this for a class, but I would read this again (which I will since I bought it). This is the first book that shows a believable encounter of a foreigner with a new culture. Generally, authors are reluctant to describe in detail all the mistakes and humiliations that occur when entering a new world, but these authors are unafraid to share their trials, but of course their tribulations as well. It is really a heartwarming tale of friendships formed in a strange and exciting world. Plus, it reads like a novel!

Parallel Worlds provides unique insights not only into the world of the Beng, but into the challenging experience of a writer and an anthropologist trying to fit in and to understand an unfamiliar culture. The two alternating voices are interwoven to create a narrative of the couple's years in the Cote d'Ivoire that allow the book to transcend categorization as strictly creative non-fiction or anthropology. Graham's passages are filled with the quiet and distinctive prose that categorizes his work as a short story writer and a novelist. Gottlieb's sections are filled with insights as she learns more about the Beng, often through complicated backward and forward steps as mistakes are made, discovered, and corrected. For readers unfamiliar with African culture, this book provides a beautiful but ultimately real portrait of Beng life as the writers become more and more part of the villiage existance. But perhaps the most interesting thread of the narrative is the gradual process of the familiar turning strange, of America existing as a paralell and unfamiliar world viewed from a distance.

Wow! Do you know what CRITTERS grow in the legs of some rural African people and whether or not you could endure helping the people remove them? Would you be able to stand eating BITTER and PASTY GRUEL and yams at every meal? This book helps you answer your own questions about personal tolerance. It forces you to think about cultural differences and ask yourself whether or not you could live in parallel worlds if you had to.

If you're looking for a great fiction story, this is not it! Rather, it is an interesting anthropological STUDY which was meant to inform, enlighten and interest. If you are ignorant about the difference between genres, then you have no right to complain about the book!

I particularly found the contrasting writing styles from chapter to chapter very refreshing. The wife, an anthropologist, writes from her perspective in a thorough (scientific) way and in alternating chapters, her husband, a fiction writer (How to Read an Unwritten Language--GREAT, by the way), offers a unique look at the Beng society through the eyes of the creative author and all-American guy next door.

I couldn't wait to read the information toward the end of the book about the trip back to the village and what became of the different Beng people later on.

This book should definitely be required for beginning social anthropology courses because it integrates a traditional ethnography with the perspective of a creative writer. In other words, it has very valid anthropology, yet will still engage the student! I can't tell you how many BORING ethnographies I read in Anthro 105 years ago when I was in college!!! I wish I had been assigned Parallel Worlds first to help me bridge the gap.