Book Description
Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The text avoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.
Customer Reviews:
Clear, rigorous, fascinating.......2007-01-20
I'm a Ph.D. student in biophysics. This is the best treatment of systems biology that I've encountered. It treats both the math and the biology with clarity, rigor, and respect. It simplifies without dumbing down. It's beautifully written. If you doubt that systems biology is a real scientific discipline, this book will change your mind.
Building Mathematical Models of Cells.......2006-09-25
The history of science over the past few centuries is to become ever more specialized. The physicists, becomming ever more concerned with the very large (stars, galaxies, the cosmos) or the very tiny (first atoms, then atomic components, now sub-components. The biologists on the other hand were studying much larger things, such as the cells that make up life. Both sciences developed techniques to facilitate their study.
In recent years, researchers have discovered that sometimes these specialized techniques can be used to develop greater insight into what is happening in other sciences.
In this book, Dr. Alon uses his training in physics to examine certain aspects of biology and to use the terminology and mathematics to describe the way these biological networks work.
The goal of the book is to begin the formulation of general laws that apply to biological networks. This is done by providing a mathematical framework in which some of the design principles of biological systems can help to understand biological networks. In looking at the results, an underlying simplicity not seen before appears in biological systems.
Great Job.......2006-09-09
A superb intro to the field. The math is moderate and helpful. Network concepts and their ties to examples and theory are clearly and succinctly presented. This is a textbook but reads easily like a book. Covers key elements while connecting them by at least mention to up-to-date further research. The basics and the grandeur of systems biology. I am trying to remember now anything on the negative side and cannot.
Book Description
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Customer Reviews:
A best book on Statistical Pattern Recognition.......2005-09-13
Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
Standard reference and a classic text but with flaws.......2004-01-20
I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows
(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important >
< -sign goes the wrong way.
good coverage for engineers.......2000-08-04
Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.
Standard Reference in the Field.......2000-04-06
If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.
Average customer rating:
- modelling financial instruments
- good analysis on data error.
- From the experts in the field
- For the new millenium...that's what we need.
- More Than An Introduction
|
An Introduction to High-Frequency Finance
Ramazan Gençay ,
Michel Dacorogna ,
Ulrich A. Muller ,
Olivier Pictet , and
Richard Olsen
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Similar Items:
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Analysis of Financial Time Series, 2nd Edition (Wiley Series in Probability and Statistics)
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Market Models: A Guide to Financial Data Analysis
-
Trading and Exchanges: Market Microstructure for Practitioners
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Optimal Trading Strategies: Quantitative Approaches for Managing Market Impact and Trading Risk
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An Introduction to Wavelets and Other Filtering Methods in Finance and Economics
ASIN: 0122796713 |
Book Description
Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure. This book discusses the best mathematical models and tools for dealing with such vast amounts of data.
This book provides a framework for the analysis, modeling, and inference of high frequency financial time series. With particular emphasis on foreign exchange markets, as well as currency, interest rate, and bond futures markets, this unified view of high frequency time series methods investigates the price formation process and concludes by reviewing techniques for constructing systematic trading models for financial assets.
Customer Reviews:
modelling financial instruments.......2007-03-08
The book gives an indepth statistical modelling of important financial events, that have time dependency. It is suitable for the financial analyst who wants a semi-empirical approach.
For some quantities, like foreign exchange data, there is a comparison between fully empirical results and various theoretical models. What is investigated are such behaviours like scaling laws, for the absolute returns as a function of frequency. Here, it has been empirically observed that scalings do exist for FX rates.
Whenever possible, the book gives rigorous results, often encapsulated in theorems relating to distributions of independently distributed random variables. The reader should have a background in statistics, with the equivalent of several years of undergraduate courses.
good analysis on data error........2007-01-16
Many type of error the book list are frequently occur in FX data.
This book give good guide on how to filter them.
From the experts in the field.......2002-06-06
Michel Dacorogna and the team at the former Olsen & Associates are well-known experts in the field of foreign exchange rate data analysis, and their book provides us with a vast, useful source of information. Unfortunately for students and other beginners, the book is written like a compilation of papers and review articles, the opposite of pedagogical, and with an awful choice of 'computerese' notation (MA(t,n)=sum(EMA(t',k)... etc) that makes Boudhaud-Potters look easy in comparison. More to the point, even their noncomputerese notation is difficult to follow. I hope for a very different second edition written pedagogically for students of this growing and important field. On the positive side, data analyses are performed using logarithmic returns, not price increments. Workers in the field who consult this text will find it helpful.
For the new millenium...that's what we need........2001-07-23
The book covers a wide range of topics related to high-frequency data in Finance. There is a very detailed approach to tackle a huge amount of data and to deal with its based stylized facts. The book triggers the reader's desire to update his knowledge in the field of finance.
More Than An Introduction.......2001-05-28
This one of the few books on high frequency finance is a most welcome to the literature. The book is useful not only for people who are new to the subject but also for researchers in the field since it is a most uniform treatment of many topics. From adaptive data cleaning (chapter 4) to intraday and weekly seasonality (chapter 6) and real time trading models (chapter 11), it covers a broad range of topics specific to high frequency financial time series analysis. Chapters on volatility modeling (Chapter 8), forecasting (chapter 9) and correlation and multivariate risk (chapter 10) are enlightening especially for risk exposure analysis and risk management purposes. Finally, the the extensive bibliography is a precious source for those who would like to explore certain topics in detail. I highly recommend it for practitioners as well as researchers in the field.
Book Description
Finance is one of the fastest growing areas in the modern banking and corporate world. This, together with the sophistication of modern financial products, provides a rapidly growing impetus for new mathematical models and modern mathematical methods. Indeed, the area is an expanding source for novel and relevant "real-world" mathematics. In this book, the authors describe the modeling of financial derivative products from an applied mathematician's viewpoint, from modeling to analysis to elementary computation. The authors present a unified approach to modeling derivative products as partial differential equations, using numerical solutions where appropriate. The authors assume some mathematical background, but provide clear explanations for material beyond elementary calculus, probability, and algebra. This volume will become the standard introduction for advanced undergraduate students to this exciting new field.
Customer Reviews:
Not bad... but there is better out there.......2007-10-23
A per its title, this is an applied mathematics book, and therefore a minimal level of math is expected from the reader. Taking a PDE approach, the book aims at presenting various methods for pricing financial options. While the first few chapters are pretty good at skimming the surface of the theory and laying down the key principles of options pricing, I find that in general, the book lacks depth. Many results (prices of barrier, lookback, asian, etc...) are simply given without real development (or simply with a little of "hand-waving") while the section on the linearity complementarity problem for the American Option is quite muddled... The book does not provide any new insight into the more difficult areas of option pricing and in that sense, simply goes through the typical presentation without adding much value.
It is nonetheless an acceptable and quick overview if you are looking for a refresher of key concepts. If you are looking for a thorough mathematical introduction to options pricing, You-Lan Zhu's book (for example) does a much better job at covering the PDE approach much more rigorously (proving for example some of the convergence criterias for the finite difference method, covering the linear complementarity approach in much more details as well as presenting other numerical techniques) without being overly formal.
Good Buy.......2007-08-29
maps one to one with many chapters in Hull. more elaborate derivations than Hull. Fixed income area treatment is very slim though. Good Buy for the Price.
Okay but not an introduction.......2006-07-31
If you want an introduction, read another book like Hull. If you want to learn how to apply Partial Differential Equations (PDEs) approach to finance then it is a useful book. However, it is better to read an elementary PDEs book before reading this book. At least, learn how to solve parabolic PDEs analytically because the technical notes in the book would not help much.
Introduction to partial differential equations in finance.......2005-10-13
This book treats only the partial differential equations
in Finance and how to treat them using Finite Differences
and Tree. For this purpose it is very well written and
understandable. A very good beginning for student. Even
undergraduate.
Now after reading it you should understand the martingales reading the baxter and how to implement Monte Carlo using, for example Glasserman (see my reviews)
A good introduction to the PDE approach.......2005-10-10
Contrary to what many readers believe, this book explains the pricing of derivatives much better than Hull. Hull gives an overview of the mechanics and properties of the derivative pricing industry, along with its pricing methodologies, and this book provides an in depth method to one of the pricing methods.
Financial derivatives can be priced by a wide range of methodologies, among some the elegant equivalent martingale measure approach (or risk-neutral pricing), replication, multinomial tree approximation, Monte Carlo simulation, partial differential equations etc etc.
This book gives an excellent introduction, and an insight to the PDE approach. Although being a big fan of the Girsanov-change-of-measure method myself, these analytical methods often fail in the valuation of highly complex derivatives like the exotics. Pricing americans prove to be hard and inefficient too, even with simulation and the risk-neutral approach.
This is where PDE methods come in. Since most derivatives (or term structures) have a PDE describing its evolution, solving the PDE seems to be a good (or sometimes the best) way, no matter how complex the derivative can get. PDEs on the other hand, have very robust and easy methods for solving. Therefore, this book brings the reader through basic PDE solving methods, analytical solutions, techniques for fast and efficient numerical approximations as well as rigorous technical explanations for some of the mathematics of partial differential equations (which arise in the financial industry).
The authors are famous for their research in the field of Industrial and Applied Mathematics, and this book continues to be a classic for undergraduates in mathematics in Oxford. If you want to have an overview of the pde approach to option valuation, without the hassle of learning up Radon-Nikodým and martingales, I highly recommend this book!
Book Description
In today's increasingly competitive financial world, successful risk management, portfolio management, and financial structuring demand more than up-to-date financial know-how. They also call for quantitative expertise, including the ability to effectively apply mathematical modeling tools and techniques. An Introduction to Credit Risk Modeling supplies both the bricks and the mortar of risk management. In a gentle and concise lecture-note style, it introduces the fundamentals of credit risk management, provides a broad treatment of the related modeling theory and methods, and explores their application to credit portfolio securitization, credit risk in a trading portfolio, and credit derivatives risk. The presentation is thorough but refreshingly accessible, foregoing unnecessary technical details yet remaining mathematically precise. Whether you are a risk manager looking for a more quantitative approach to credit risk or you are planning a move from the academic arena to a career in professional credit risk management, An Introduction to Credit Risk Modeling is the book you've been looking for. It will bring you quickly up to speed with information needed to resolve the questions and quandaries encountered in practice.
Customer Reviews:
read this before going for it.......2007-04-23
Well first off I would like to tell anyone who doesn't have a solid working knowledge of calculus (including multivariate) to avoid this book as it requires multiple integrals and infinite series and sequences. Now onto the good and the bad:
THE GOOD:
This text explains concepts very well and is FULL of examples. I mean literally 3/4 of the book, maybe more, is examples. Every chapter also has a section of problems that have partial solutions, which can come in very handy. This is pretty much all that is good about this text, but keep in mind that explaination is the most important part of any textbook.
THE BAD:
The proofs skip plenty of steps. And I mean plenty, so much that a proof in the book would take 5 lines but when my professor proved it in class it would take him nearly 15. Also while there are tonnes of examples, too many are theoretical and very hard. The book costs a hefty amount of change and is suprisingly small, Author couldl have given few more examples to make it interesting. However the worst thing about this book is how the author leaves important things in with the text often. However most these things are small, and overall the text is a good intro to probability theory.
a very good book.......2006-10-31
The authors wanted to write the book that they themselves would have liked to read before starting a profession in risk management. I am working for a treasury consultancy firm. This book was the best of the five I bought. The text is very clear yet does not assume too much prior knowledge. It covers theory as well as industry practice. The book contains much advanced statistics and readers must have some background in order to handle this. The authors keep it simple but not too simple. Their approach is pragmatic throughout. I am really happy to have read this book when I started doing work in credit risk management.
good combination of math and finance.......2006-02-22
As indicated on the back of the book, the authors are aiming at audience who have some knowledge in both math and finance but may be weak in one and strong in another. Either way, this is a good book to read on credit risk.
Clear and comprehensive.......2005-10-27
This book clearly articulates basic concepts of credit risk modeling. At the same time it is mathematically rigorous. This book enables non mathematician with some (basic) knowledge in probability statistic to better understand and develop his risk management skills.
A good read!.......2004-08-19
Easy to understand with not a tremendous amount of complicated math to dicipher. Just what the doctor ordered.
Book Description
Ross's classic bestseller,
Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.
A new section (3.7) on COMPOUND RANDOM VARIABLES, that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions.
A new section (4.11) on HIDDDEN MARKOV CHAINS, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states.
Simplified Approach for Analyzing Nonhomogeneous Poisson processes
Additional results on queues relating to the
(a) conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system,;
(b) inspection paradox for M/M/1 queues
(c) M/G/1 queue with server breakdown
Many new examples and exercises.
Customer Reviews:
one of the best introduction to probability and stochastic processes.......2007-08-20
Understanding probability requires various resources to read. I think this book is one of the irreplaceable element in these resources. It is an introduction book as the name implies. Examples are illuminating the subject very well.
Why are there so many examples?.......2007-04-01
Extremely difficult to dig through the excessive examples in order to find the relevant theorems and results. Because of this, the problems at the end of each chapter become exercises in tedium, as more time is spent searching for the necessary theorems in the text than in actually working out the solution.
I do not recommend.
One of the most accessible and engaging text books I've read.......2007-02-16
During my undergraduate career I've had the opportunity to spend several thousand dollars on textbooks--many of which have pertained to mathematics in some way. Most of these books, including those concerned with statistics and probability, have been interested in either delivering pure theory or an unending supply of problem sets (with little or nothing in the way of instructive content). Thankfully Ross's book defies these conventions.
By presenting the material in large sets of well explained and genuinely interesting problems, the book avoids being bogged down by excessive theory or volumes of sterile exercises. As a result, the book is unusually easy to read, and quite useful when it comes to clarifying or augmenting what has been taught in class.
very good.......2006-11-14
I used this book for a graduate-level course in Stochastic Processes taught by Dr. Sheldon Ross himself. I must say that I never liked probability and stochastics until I read this book. Reading it is a pleasure! The topics are presented in a highly methodical manner, with plenty of examples and exercises. The exercises are presented in a gradation. Covers a wide range of topics, and is very helpful for a course in stochastics, especially for a student who doesn't have a strong background in P & SP. This is the book to own, don't miss it!
Good development of intuition, but not as good for other purposes..........2006-09-28
I have many of the same criticisms of this book that I do of Ross's book titled: "Probability: a first course". This book reviews most of the material from that book at a faster pace and then goes into other topics. Ross in the introduction states that his main goal in this text is to develop the reader's intuition for probabilistic reasoning. This book is excellent towards acheiving this goal, but not very good for anything else. It is a very "pure" probability text, completely ignoring the fact that the field of statistics exists and is useful. At the same time, it is somewhat weak on theory. Measure theory isn't mentioned, and the emphasis overall is on computation and problem solving, not proving theorems and understanding theoretical connections between different ideas.
This book has too many examples and not enough discussion. While the examples are usually well-executed, and while I think examples are important in probability, I think it's also important to talk about the abstract development of the subject. In my opinion, more prose and fewer examples would improve the quality of this text.
Another criticism I have of this book is that this book focuses exclusively on probability, refusing to touch statistics even with a ten foot pole. While this in itself is fine, I think this book misses numerous chances to pave the road towards the later study of mathematical statistics. The result is that someone reading this book will not be particularly well prepared for studying statistics, even though the fields of probability and statistics are intimately tied to each other.
Lastly, this book has gone through too many editions--one of the reasons I rated it 3 stars instead of 4 is that I believe that there has been almost no noticeable improvement in the last two editions (I have not read any farther back than that so I can't say more). I think this is a money-making scheme on behalf of the publisher, and I think this reflects poorly on the author and publisher alike.
Average customer rating:
- Outstanding, sophisticated, unconventional classic
- Outstanding book, but you should buy the newer edition, not this version
- Buy the 2nd edition of this over Montgommery's Book
- Get a more recent title for the important modern advances
- very useful
|
Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building
George E. P. Box ,
William G. Hunter ,
J. Stuart Hunter , and
William Gordon Hunter
Manufacturer: Wiley-Interscience
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Binding: Hardcover
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Similar Items:
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Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition
-
Design and Analysis of Experiments
-
Doe Simplified 2E: Practical Tools for Effective Experimentation
-
Design of Experiments for Engineers and Scientists
-
Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics)
ASIN: 0471093157 |
Book Description
Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking'' by using examples drawn from actual case studies.
Customer Reviews:
Outstanding, sophisticated, unconventional classic.......2005-12-19
George E.P. Box, the senior author of this magnificent example of great teaching for adults, is one of the great statisticians of modern times. He is a master at teaching those with experience, especially industrial experience, but not necessarily the most advanced mathematical training. My own background in econometrics and decades of work experience left me in a position of having too little knowledge to apply sophisitcated statistical methods to experiments and too much knowledge to settle for the exposition of statistics in many experimental design texts, especially those for behavioral scientists. I had read some of Mr. Box's "Evolutionary Operation" [with Norman Draper] ("EvOp") (also outstanding, practical, and unusual) and looked at "Bayesian Inference in Statistical Analysis" [with George Tiao] ("BISA") and hoped the book was as practical as EvOp rather than as mathematical as BISA. It has turned out to be so without being unsophisticated.
Once you have mastered this, I am sure you will be prepared for many of the challenges of applying statistics to practical industrial and experimental situations and for more advanced and modern methods that have emerged since 1978 with the ubiquity of very cheap computing power.
What it may lack in the most contemporary methods it more than makes up for by helping the reader develop a good intuition for applying statistical methods and judgment.
Outstanding book, but you should buy the newer edition, not this version.......2005-07-27
All of the reviews on this book are generally consistent in their praise for the book and the authors. I do not have any points to add to the discussion other than this:
It is a credit to this version of Statistics for Experimenters that it has remained relevant throughout the years as a classic introductory text that has kept selling consistently since it was released in the 1970's. Nevertheless, unless you have a particular reason for purchasing this version, you should purchase the updated version(also available through Amazon).
The full title of the newer edition is:
Statistics for Experimenters: Design, Innovation, and Discovery, 2nd Edition
The 2nd edition, written in the same engaging and readable style as the 1st, contains virtually all of the content of the 1st edition plus advances in design of experiments that have happened since the 1st edition was published.
Buy the 2nd edition of this over Montgommery's Book.......2005-03-30
I used the Montgomery DOE book as an undergrad...but chatting with a Stat prof freind of mine..she recommened Box Hunter & Hunter over this. I had never covered the entire book..& was reading up on Factorial designs...I went ahead and bought Box Hunter & Hunter...(do wait & buy the 2nd edition due out in May-I think Amazon trys to sell you the old inventory if you are not careful)...nonetheless, the old edition I bought actually is much more intuitive and easy to follow that the "Design and Analysis of Experiments" book by Montgomery....I think its b/c the latter is written by an engineer..no offense to you out there...just that engineers cover so much material that there texts seem more "cookbook" like..here's how...w/ no too much intuition as to why ...probably catering to the engineer who has not the time to care about the why...I am thoutoughly enjoying the read...some of the quotes in hte book are pretty funny yet all the while relevant...
Get a more recent title for the important modern advances.......2004-05-05
A solid excellent DOE book, however due to it's age, it obviously does not cover more recent topics, such as mixture experiments. I've run into a few chemical engineers that have read only this book and have no idea what mixture experiments are, and why they are important in their DOE work. Also, I do not remember seeing any material on split-plot designs, and this topic is very important in industrial experimentation since most experiments are split-plots whether you know it or not, and you cannot evaluate them as normal. This is no fault of the book due to its publish date, but a newer book, such as Montgomery's or Hamada & Wu should also be read through to learn about the more recent advancements in DOE.
very useful.......2003-10-22
A great book to understand the theory and the application of statistics. The examples used in each chapter are very useful in understanding the concept.
I suggest this book to every researcher and instructer to keep it on their desks. "This" is the reference.
Book Description
In recent years the growing importance of derivative products financial markets has increased the demand for mathematical skills in financial institutions. The purpose of this book is to introduce the mathematical methods of financial modelling to provide a clear explanation of the most useful models. Introduction to Stochastic Calculus begins with an elementary presentation of discrete models, including the Cox-Ross-Rubenstein model. This book will be valued by derivatives trading, marketing, and research divisions of investment banks and other institutions, and also by graduate students and research academics in applied probability and finance theory.
Customer Reviews:
Very good.......2007-07-03
I am quite familiar with this book since I enjoyed it when it was used (along with many other good books as it should) in Purdue Computational Finance program. I got to do a number of exercises from it. Some Matlab code is available on my website (click on my name above).
A very efficient book for the right audience.......2007-01-21
Introduction to Stochastic Calculus Applied to Finance, translated from French, is a widely used classic graduate textbook on mathematical finance and is a standard required text in France for DEA and PhD programs in the field.
Most folks familiar with Steve Shreve's Stochastic Calculus Models for Finance will be surprised at its brevity, for this work is aimed at different audiences.
Whereas Shreve's work is aimed at mathematicians and physicists who are coming to finance, and building on the commonalities of understandings of time series and data sets and signals, Lamberton & Lapeyre's work is aimed at an audience of mathematically trained engineers, who look at data sets as information for solving problems. Shreve's work, is, therefore, to help people come up with mathematical proofs, and L&L's is to help people solve problems.
Both probabilistic and partial differential equation approaches are covered, so both those from electrical and telecommunication engineering and mechanical engineering will be satisfied and on familiar ground. Numerical and algorithmic methods are also covered for those with systems analysis and operations management backgrounds.
This book, however, is decidedly for those who have had significant mathematical training. Whereas with Hull, Wilmott, Neftci, or Joshi you can play around with their approaches almost instantly in Excel or other programming tools (VBA, C, etc.), Lamberton and Lapeyre's work is for those who think out loud with a white board and others do the dirty work of coding. This work lacks specific examples, data sets, etc. Which makes it difficult to place. Its clarity and brevity are welcome, and it expands the knowledge beyond Hull of those who are not trained in math and came up the practical coding grunt side of quantfin. But it also is not a complete theoretical treatment for the first string math and theory set.
In short, the book is what it is: a short primer on a large area of mathematics in finance for those well-trained in a variety of engineering and applied mathematical subjects. In other words, this book is for the French, because all the best French students are always Engineers first and something else afterwards. If you also happen to be trained as an engineer and find Hull, Wilmott, Joshi & Neftci too easy, and Shreve too hard, then this is the book for you. Or if you are like me, and you've banged your head against this stuff for years just through the happenstance of your career and want to see how a mathematician writes about your gritty world, this is a great book for shedding light in areas filled with cobwebs.
Clear and concise introduction to mathematical finance........2001-07-25
This book, translated from French, is by now a classic graduate textbook on mathematical finance, and provides a clear and concise introduction to the basic and important aspects of the theory. Although one of the first textbooks on the subject, it still remains in my opinion one of the best.
The book has been written for engineering students not mathematicians and avoids the theorem/proof format, going straight to essentials.
Also, while most textbooks on mathematical finance exclusively adopt either a probabilistic (like Baxter & Rennie) or a PDE approach to the theory (Wilmott et al, Wilmott), this book maintains the balance between the two aspects. Moreover, it does not neglect numerical methods and gives details on several algorithms for option pricing ( trees, Finite Difference, Monte Carlo) Finally, and perhaps this point is very important, the book maintains a reasonable volume while treating all these topics AND maintaining a high level of scientific rigor: all statements and notations are precise and oversimplification is avoided. Advanced topics such as variational inequalities for American options and HJM theory of interest rates are also included.
Some drawbacks of the book are: - a complete absence of empirical data/ real life figures - no description of various kinds of derivative products, why they are used,... But then, what can you ask for in such a small volume?
If you are an engineering/maths student and you want to discover what mathematical finance is about, I recommend you this book instead of John Hull's book.
A good INTRODUCTION to ONE part of finance.......1999-03-14
As precisely mentioned in the title, this book is only an introduction; and it is not an introduction to finance, but to stochastic calculus applied to finance.
The buyer of this book should therefore be aware of three facts:
1. After having read this book you are not (yet) an expert on stochastic calculus applied to finance. You have to continue with other books mentioned in Lamberton/Lapeyre. But this book is an excellent framework that leads you to many important results, omiting proofs that are only technical.
2. Mathematics is used in many other areas of Finance too (Time Series Analysis for example). What is treated in this book is only a very small part of Finance Mathematics, but an important one.
3. One should read another book with more economic background at the same time.
The authors begin with discrete-time models to present many important ideas in a (mathematically) simple environment before treating the contiuous models. Introduction to stochastic integration and stochastic differential equations is brief. Stochastic integration is only with respect to the standard browning motion. After having reached the Black-Scholes model and american options, the approach via partial differential equations is treated, followed by interest rate models, models with jumps and, a good idea: a chapter on simulations.
The book has very few mistakes, no important ones, only a strange layout failure on pages 6 to 7.
So I highly recommend this book as an INTRODUCTION to ONE important part of finance mathematics if read in combination with another book with more economic background. It can especially be used for upper graduate student seminars or as a basis for lecture courses.
A stochastic approach of finance for engineers!.......1998-07-28
The french initial version of this book has been one of my first technical papers that deal with stochastic calculus towards finance. It is written by and for engineers I must admit, but students in actuarial sciences (like me) won't be lost by so many formulas and equations if they agree to read with a piece of paper and a pencil on the hand. I have worked on the Vasicek's model and the simulations described have helped me a lot. Too bad that the lattice model is not explored. Anyway it is a good preparation before the opening of "Brownian Motion and Stochastic Calculus" from Karatzas & Shreve.
Book Description
This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory. Some chapters have their own appendices containing the more advanced topics and/or difficult proofs. Moreover, there are three appendices with material that is supposed to be known. Appendix I contains a comprehensive review of linear algebra, including all the proofs. Appendix II reviews a variety of mathematical topics and concepts that are used throughout the main text, and Appendix III reviews complex analysis. Therefore, this book is uniquely self-contained.
Customer Reviews:
Highly recommended - a joy to read . . ........2005-01-07
If you are looking for an introduction to financial option valuation that is well-written and well-referenced than this book is for you. Prof. Higham is an excellent author (I highly recommend his other books Learning LaTeX and MATLAB Guide) and so anything he writes is a joy to read. His latest book is no exception. It is full of figures that help bring the equations and the ideas to life. Like many of his technical papers (which I also recommend you read - they are available at his website), he has incorporated MATLAB (a powerful matrix manipulation and numerical simulation tool) codes throughout the book (not only does he provide code listings but you can actually download the codes and run them assuming you own the software or have a license - I have!). The codes are a great way to see the equations in practice if you don't have MATLAB and experiment with some of the key parameters yourself if you do. Regarding the subject of the book itself, let me say that I am in the mechanical engineering field and can barely balance my checkbook - ok, my wife does it for me) but I am interested in all things mathematical and find the subject of option valuation (and the possibility of making some extra money) enticing. The book clearly introduces topics related to random numbers and stochastics, as well as finite-difference approximations for partial differential equations. The ultimate goal is the Black-Scholes PDE which is treated in the later half of the book. Monte Carlo simulation techniques as applied to finance are covered as well in several chapters. What I really enjoy about this book (and his other books) is the way he actually tries to teach and advise the reader - a good writer must be sensitive to his/her audience - and this is most appreciated by myself and others I am sure. The bottomline is that this is the first book to own if you want to get into the field of computational finance (his references tell you where to go next). I highly recommend it.
A good hands-on intro to option valuation.......2004-12-05
There are a lot of derivatives books out there - most of them follow the same approach. This one's different: no complicated measure-theoretic probability theory (of absolutely no use to practitioners), but lots of hands-on Matlab examples. A very reasonable price too. My only suggestion to the author would be to provide more appropriate names to his Matlab functions (instead of chapter numbers) - but this can easily be changed by the reader.
Book Description
Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.
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