Book Description
Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques.
This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios.
The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential.
The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.
Mathematical Reviews, 2004: "... this book is very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context."
Customer Reviews:
Review for Monte Carlo Methods... by P. Glasserman.......2007-07-16
The book is just right for a reader who is looking for state-of-the-art techniques in Monte-Carlo methods in general. The fact that the book is specific to financial systems does not limit the usability of the book in the manner it is written. There are a lots of useful references one can get out of this book.
The book is for advanced readers in the sense that it requires rigorous mathematical ability to understand all the concepts. It is by no means for a novice reader and requires background in computational mathematics.
Best financial engineering book on MC.......2007-06-29
This is like the bible of Monte Carlo methods in financing. Both a good read and a good reference book. Must have! for any quant on wall street.
good book on Monte Carlo in Finance.......2007-04-02
But it seems the author is a little focused on selling his ideas, but not a very subjective overview of all topics in M-C method in finance.
Excelent choice on finance Monte Carlo.......2007-03-08
Clear and sound theoretical background on applied Monte Carlo for finance.
Brilliant.......2006-12-26
Almost everything related to Monte Carlo in Financial Engineering is covered at just the right level of detail. Quite easy to read too.
Book Description
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available.
The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
Customer Reviews:
It ain't bad, it ain't great, it ain't complete, but it ain't wrong...........2007-03-27
What this book is:
1) Dated. PJ wrote this book in 2002, using thoughts and techniques applicable to a Pentium 4 Xeon world (2001). In 2002 folks often ran option book position MC simulations *overnight.*
* Also, GOOGLE Scholar wasn't out yet....if you wanted to collect all the papers and abstracts on MC methods in 2002 you had to talk to a librarian.
2) Basic. Well, now it is basic....but when it first came out it was sharply focused on finance and it was three years ahead of Glasserman's book.
3) This book is okay for what it is, which is a topical outline, some lecture notes introducing a reasonably well math trained audience to MC and finance. In 2001 MC was a cutting edge new thing. People forget what 2001 was like: Heck, one bank was flogging that it had a 200 node binomial model programmed in Excel available for customer use on an *appointment* basis. That was the state of things at the time.
What this book is not:
1) a cookbook. There is no "cut and paste" code in here. In 2001-2 believe it or not code was made by the sweat of your brow and was considered highly proprietary. Okay so in 2007 we just cobble together Franken-code and debug, but that wasn't the way it was in 2002. There weren't "Numerical Recipes in [code flavour of the month] sites. And folks were fired for showing code ot other people.
2) It won't teach you math. You are supposed to have learned a lot of the stuff this assumes you know.
3) It won't teach you programming in [pick your language] or its step-daughters.
4) Complete. This is expanded lecture notes. Is every low discrepancy method covered? (and all its weird names) No. Is every Greek covered and every possible expression? No. Is every application covered? Hmmm, still looking for that hybrid bond model in here.....not even in the index.
5) A replacement for work. This is a "topics in" and "helpful directions" and "friendly discussion" book. It does not solve your problem on your platform for your goals. It also won't wipe your rear end, buy you beers, tuck you in at night, or let you call it "Rosie." As in Rosie fingers and Harry palm.
So what is this book good for? Well, it is a not too bad a primer, it builds your vocabulary and helps your conceptualization of goals and purposes, and if you move on to Glasserman your comprehension will be much higher, although I'm not sure he covers that much more that much better.
But if you come from a science or math background that has used MC for other purposes, and you know programming, you can probably figure out most of what PJ covers on your own.
for Quants only.......2003-06-24
if you're a quant, you might really love this book
if you're a person who wants to have a "basic" understanding how to use MC for consulting or product pricing with examples, you got the wrong book (not mentioning that your maths must be pretty good).
if you're looking for an Excel example on how to price some basic options, i highly recommend Jackson & Staunton or Wilmott.
Good book.......2003-05-27
This book is pretty good as it covers lots of different areas of Monte Carlo simulation and some of the newer stuffs, such as copulae, etc. The math presentation is brief but to the point as application of the mathematics to Monte Carlo methods is the emphasis. Intuitive ideas behind the formula is explained pretty well as it tells you where certain formula can be used for. It would be helpful to have taken an advanced course in Monte Carlo methods in Finance to appreciate the book. I would personally suggest Glasserman's course at Columbia U. Prof Glasserman is also writing a book on the subject that he uses for lecture notes now. It would turn out to be an even better book to read.
An advanced approach to math methods behind finance.......2002-09-19
Very interesting and well written book reviewing more advanced mathematical concepts which might be relevant for finance engineering - not limited to Monte Carlo methods. The author seems to have a firm background in theoretical physics. Definitely not for simpletons.
CD does not work.......2002-08-29
It is a book for mathematics lovers not financial oriented profesionals. I would not recomend this book for those looking to gain more practical knowledge on this subject.
Book Description
This highly accessible and innovative text (and accompanying CD-ROM) uses Excel (R) workbooks powered by Visual Basic macros to teach the core concepts of econometrics without advanced mathematics. It enables students to run monte Carlo simulations in which they repeatedly sample from artificial data sets in order to understand the data generating process and sampling distribution. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software.
Customer Reviews:
Blows Away All Other Intro Texts.......2006-05-31
I am only half finished with this book, but since there is only one other review, I want to get my thoughts up NOW. I may add to them when I have finished.
My wife is an econ major at a small school with very few econ majors. Econometrics is not offered as a course. Although as a practical businessman with a preference for Austrian school economic theory I have a healthy scepticism about quantitative macroeconomic (especially) formulas, I have told my wife that she can not be a part of today's theoretical discussions without a basic understanding of econometrics. I promised to help her self-study this topic, and have reviewed a number of supposedly "introductory" texts (to remain nameless, but they are standards)that have lost me within 50 pages. Neither my wife nor I have calculus or matrix algebra. However, even those texts that say they do not rely on such math knowledge are still confusing. Until now.
Barreto's text is a wonder. The other review gives solid examples of why this is. Let me just say that you will be able to see econometric principles in action. The explanations are incredibly clear, and the work on the beefed up excel spreadsheets effectively demonstrates those explanations. I know this will be difficult to believe, but the text is actually fun to read. My wife and I both have college algebra, business statistics, and basic excel. That's all you need to use this book.
Every university should adopt this book as the intro econometrics text. It provides an approach to learning the topic that is accessible to any intelligent econ student. Those going on to PhD work could supplement with calculus, matrix algebra, and one of the other so-called intro texts. Barreto provides a way for normal econ students to understand econometrics, something that all econ students should be required to do. (Even though much of econometrics is nonsense, knowledge of its applications and mis-applications is still the ticket to being taken seriously in economic debate.)
I only wish I could give this book more than 5 stars. It is a stunning achievement.
Interactive Guide to UNDERSTANDING econometrics.......2006-02-16
When I was a new graduate student I ended up buying several different econometrics texts. No one text had the best explanation for each topic. The problem remained that for many topics I never did find a book which translated the formal mathematical presentation into a practical worked out example, so that I could understand the procedure and how to implement it.
This book and accompanying CD-ROM does that and much more.
Every topic includes guided Microsoft Excel spreadsheets and add-ins which illustrate the topic being addressed. The text clearly explains not only the HOW, but the WHY. The economics and the econometrics are presented with such clarity and unity; bridging the two in a way that none of the other texts do.
In Barreto and Howlands book/CD package you interact with the data and the graphs (they include a superior add-in for creating histograms), and run Monte Carlo simulations to see the behavior of the estimators in repeated sampling. These are "live" spreadsheets that invite you to experiment. For example; there is an Excel workbook which illustrates the correlation coefficient. Rather than a dry recitation of formula and proof, you can interact with the spreadsheet and see exactly how the same coefficient can apply to data having very different patterns. It is one thing to see an illustration, and quite another to actually be the one creating the diagram, simply by running the macros and changing parameters. This "hands on" approach is so vital to actually getting an understanding of the material. I have only a basic understanding of Excel, and have had no difficulties in using the workbooks.
While the limits of Excel are pointed out by the authors, it is important to note the reason for using Excel. It is widely understood and available; there is no learning curve. By using Excel there is no software barrier between the student and understanding the principles of econometric modelling. In less than 1/2 hour I took the data and example of a Probit model using Maximum Likelihood estimation from a course web site from across the country and replicated the results using the add-in provided. Most of that time was used to extract the data from a .pdf file and get it formatted properly for Excel. Once I had the data in Excel, it took less than 2 minutes to run the Probit estimation (my first time using that add-in!) By the way, the results using the authors add-in for solving Probit models with ML estimation were the same as the results from GAUSS code to do the same. The add-in had a distinct advantage though in that a choice for Probit or Logit model estimation using either Non Linear Least Squares or the Maximum Likelihood estimation was just a radio button away! This text can complement any course, regardless of the software used.
Again, the beauty of the book is that you are not just left with Greek formulas that leave you wondering how to do the computations, and you are not left with computer output leaving you to wonder how to interpret that output. The text explains the meaning so powerfully that you are not only armed with an understanding which is useful for success in your course work, but also for applying the quantitative tools in real world analysis and applications. The text is like going to see your favorite professor who is sitting there with you one on one, giving you insight which only comes from experience.
I've been through courses that use Greene, and Judge, as well as introductory texts. This text stands alone in making use of the computing power we have at our disposal today, not to produce more computer printouts, but rather to increase our understanding--providing the sound reasoning for applying that power.
I should add that even after two years of statistics and econometrics I learned quite a lot from the statistics review chapters. Don't be misled by the "Introductory" title. I had learned and executed Artificial Neural Network models in graduate courses, but still learned a lot from the section on correlation in this book. For undergrad students this book will put you on the right path. For grad students it will correct blind spots and misconceptions.
I highly recommend this book/CD package to any econometrics student and to practicing analysts that use regression analysis. The authors have created a product that I wish I had when I was in school, but am glad I found now for applying in my career.
Detailed info on contents as well as the Excel files and add-ins are available from the authors' web site which I found prior to ordering from Amazon. Once I tried the workbooks, I knew I wanted the book. It's 800 pages of solid information and inspired teaching.
http://www.wabash.edu/econometrics/index.htm
Book Description
Risk Analysis A Quantitative Guide Risk and uncertainty are key features of most business and government problems and need to be understood for rational decisions to be made. This book concerns itself with the quantification of risk, the modelling of identified risks and how to make decisions from those models. Following on from the success of the previous edition of this clearly written and highly regarded book, this edition is extensively revised and updated and will provide an invaluable practical guide for beginners and experienced practitioners alike. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modelling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems. Reviews of the first edition "It identifies the various facets of risk analysis and provides a valuable reference to the concepts and techniques employed." Project, 1997 "It clearly explains many essential aspects of quantitative risk analysis . provides valuable techniques and sound professional advice." Journal of Behavioral Decision Making, Vol. 12, 1999 "The book offers a powerful method for dealing with risk and uncertainty." Zentralblatt für Mathematik, Band 908, 1999
Customer Reviews:
Risk Analysis.......2006-05-24
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.
Best Book for Quantitative Risk Analysis.......2004-04-25
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.
1st edition more useful to a practitioner than the 2nd.......2003-10-18
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!
Rigouros, clear and practical.......2003-04-20
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.
Risk Analysis: A Quantitative Guide.......2001-08-25
I purchased this book to learn to write simulation equations in excel but only found it was a manual ( type book ) with good information for a very expensive software I did not have....If you have RISK software, it is a great book to have... I returned my copy w/o scanning the entire book.
Book Description
Simulation and Monte Carlo is aimed at students studying for degrees in Mathematics, Statistics, Financial Mathematics, Operational Research, Computer Science, and allied subjects, who wish an up-to-date account of the theory and practice of Simulation. Its distinguishing features are in-depth accounts of the theory of Simulation, including the important topic of variance reduction techniques, together with illustrative applications in Financial Mathematics, Markov chain Monte Carlo, and Discrete Event Simulation.
Each chapter contains a good selection of exercises and solutions with an accompanying appendix comprising a Maple worksheet containing simulation procedures. The worksheets can also be downloaded from the web site supporting the book. This encourages readers to adopt a hands-on approach in the effective design of simulation experiments.
Arising from a course taught at Edinburgh University over several years, the book will also appeal to practitioners working in the finance industry, statistics and operations research.
Book Description
This book puts numerical methods into action for the purpose of solving concrete problems arising in quantitative finance. Part one develops a comprehensive toolkit including Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transform-based methods and quadrature techniques. The content originates from class notes written for courses on numerical methods for finance and exotic derivative pricing held by the authors at Bocconi University since the year 2000. Part two proposes eighteen self-contained cases covering model simulation, derivative valuation, dynamic hedging, portfolio selection, risk management, statistical estimation and model calibration. It encompasses a wide variety of problems arising in markets for equity, interest rates, credit risk, energy and exotic derivatives. Each case introduces a problem, develops a detailed solution and illustrates empirical results. Proposed algorithms are implemented using either Matlab
® or Visual Basic for Applications
® in collaboration with contributors.
Book Description
Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields.
Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon.
This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.
Download Description
"Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields.
Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon.
This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today."
Customer Reviews:
Contents are good, typos are terrible........2007-08-29
I have an advanced degree from physics, and found that the contents in this book are well balanced between fincial math and physical/financial models. The author definitely knows how to cut short at various points where very advanced math (even beyond my phd degree in physics) are needed. Instead, the author smartly explain only some fast results or theorems. This really makes them very easy to go through, and lets readers focus on the models instead of rigious math. The MATLAB codes are very helpful as well. Question problems are designed very well too.
Downsides: Typos are terrible. On some pages, typos make it impossible to read. But it's fun to figure them out. Also, at several points, the author used very confusing symbols, such as he used "j" for the maturity time of option (pg 108). Also, in some sections, the logic of writing was very vagous, i.e., you will read some results without knowing what they are here for, but you will see that they are used at the end of the section. Then you have to go through the whole section quickly to orgonize the content in correct order in your head.
Generally, it's a nice little book to have. And very helpful to know MC methods in finance.
Book Description
Applying practical tools to the volatile process of negotiating
Prognosticators apply Monte Carlo Analysis (MCA) to determine the likelihood and significance of a complete range of future outcomes; Real Options Analysis (ROA) can then be employed to develop pricing structures, or options, for such outcomes. Richard Razgaitis' Dealmaking shows readers how to apply these powerful valuation tools to a variety of business processes, such as pricing, negotiating, or living with a "deal," be it a technology license, and R&D partnership, or an outright sales agreement. Dealmaking distinguishes itself from other negotiating guides not only by treating negotiations as an increasingly common situation, but also by presenting a tool-based approach that creates flexible, practical valuation models. This forward-thinking guide includes a variety of checklists, case studies, and a CD-ROM with the appropriate software.
Richard Razgaitis (Bloomsbury, NJ) is a Managing Director at InteCap, Inc. He has over twenty-five years of experience working with the development, commercialization, and strategic management of technology, seventeen of which have been spent in the commercialization of intellectual property.
Download Description
Applying practical tools to the volatile process of negotiating
Prognosticators apply Monte Carlo Analysis (MCA) to determine the likelihood and significance of a complete range of future outcomes; Real Options Analysis (ROA) can then be employed to develop pricing structures, or options, for such outcomes. Richard Razgaitis' Dealmaking shows readers how to apply these powerful valuation tools to a variety of business processes, such as pricing, negotiating, or living with a "deal," be it a technology license, and R&D partnership, or an outright sales agreement. Dealmaking distinguishes itself from other negotiating guides not only by treating negotiations as an increasingly common situation, but also by presenting a tool-based approach that creates flexible, practical valuation models. This forward-thinking guide includes a variety of checklists, case studies .
Richard Razgaitis (Bloomsbury, NJ) is a Managing Director at InteCap, Inc. He has over twenty-five years of experience working with the development, commercialization, and strategic management of technology, seventeen of which have been spent in the commercialization of intellectual property.
Customer Reviews:
Weak Attempt and Misapplied Theories.......2007-01-11
There are only 3 out of 12 pertinent chapters of this book. The other 9 chapters are an extraneous rambling to take up space on paper. The author even goes as far in this irrelevant material to discuss topics such as art collecting. The pertinent chapters are not useful because of misapplied theory. For example, the author does not present the Black-Scholes methodology correctly and improperly defines profitability of an "in-the-money" option. To me this is a marketing ploy through Decisioneering.
I recommend books by Johnathan Mun, Robert Hull, Richard Brealey, and the CBOT.
Not too helpful.......2006-01-11
The title made buying the book interesting, but I was kind of disappointed when I purchased the book. For someone with a good quantitative background or some real experience dealing with real options and monte carlo simulation, this book would prove to be frustrating. It takes shortcuts by using (to the point of promoting) the Crystal Ball software by Decisioneering. Unfortunately, the book doesn't come with a trial software of Crystal Ball like some other real options books, so you only have to rely on the pictures in the book. Aside from this, the discussion of many topics is very watered-down. The author doesn't go in too much depth, and there is not much takeaway from this book. I suggest buying a different book for your needs.
A Results-Based Approach.......2004-10-06
While preparing for a negotiation the objective is to identify risks and rewards. The point of the negotiation is to capture value while finding terms value-enhancing to all parties.
Richard Razgaitis combines Real Option Analysis and Monte Carlo Analysis to provide practical tools and procedures. Used properly, these models will provide data that will assure quick, predictable and reasonable outcomes.
Average customer rating:
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Life-Cycle Costing: Using Activity-Based Costing and Monte Carlo Methods to Manage Future Costs and Risks
Jan Emblemsvåg
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Life Cycle Costing for Facilities
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Cost & Effect: Using Integrated Cost Systems to Drive Profitability and Performance
ASIN: 0471358851 |
Book Description
Everyone jokes about the 20/20 hindsight of cost management. In Life-Cycle Costing, Jan Emblemsvag proposes to do something about it.
Here's a new approach to life cycle costing that brings activity-based costing, risk, and uncertainty into the forefront. You'll focus on future costs and learn how you can perform any type of cost management activity better than before by introducing uncertainty into models and exploiting them to the max.
Order your copy today!
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Applications of Monte Carlo Methods to Finance and Insurance
Thomas N. Herzog , and
Graham Lord
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