Numerical Methods in Finance and Economics: A MATLAB-Based Introduction (Statistics in Practice) Review

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(More customer reviews)I am using this as a secondary reference for a half-semester Matlab and Optimization course and it has been invaluable. The writing is crystal clear, the examples and code are pretty close to perfect for every section. The author writes in a very intuitive fashion and of the sections I have covered I don't think I have been lost or confused once, which in this field is uncommon.
This is not really an introductory book for finance and if you read the preface, Brandimarte does explain that the book complements and does not replace more specific texts. I have been seen most of the material in this book covered in at least a cursory fashion in my Financial Engineering program and it makes a difference, so I would recommend that you are familiar with the material covered in Hull's "Options, Futures and Other Derivatives" or Neftci's "Principles of Financial Engineering" and Neftci's "Introduction to the Mathematics of Financial Derivatives" or similar texts.
You also won't be able to get away without having at least some intermediate level linear algebra. You don't have know it well but concept such as conditioning, LU and cholesky factorization should ring a bell. On the other hand some topics such as optimization I feel are covered very solidly. I am using "Optimization Methods in Finance" by Cornuejols and Tutuncu and although it is a great book I have to say that the examples in Brandimarte's book are much better and more intuitively explained, although clearly not in the same detail.
My only gripe with the book is that he tends to use code from the toolboxes, which can be inconvenient if you are student and only have the student version at home. Most of the time he builds the code from scratch but he uses toolbox code enough that it is annoying.

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A state-of-the-art introduction to the powerful mathematical and statistical tools used in the field of financeThe use of mathematical models and numerical techniques is a practice employed by a growing number of applied mathematicians working on applications in finance. Reflecting this development, Numerical Methods in Finance and Economics: A MATLAB?-Based Introduction, Second Edition bridges the gap between financial theory and computational practice while showing readers how to utilize MATLAB?--the powerful numerical computing environment--for financial applications.The author provides an essential foundation in finance and numerical analysis in addition to background material for students from both engineering and economics perspectives. A wide range of topics is covered, including standard numerical analysis methods, Monte Carlo methods to simulate systems affected by significant uncertainty, and optimization methods to find an optimal set of decisions.Among this book's most outstanding features is the integration of MATLAB?, which helps students and practitioners solve relevant problems in finance, such as portfolio management and derivatives pricing. This tutorial is useful in connecting theory with practice in the application of classical numerical methods and advanced methods, while illustrating underlying algorithmic concepts in concrete terms.Newly featured in the Second Edition:* In-depth treatment of Monte Carlo methods with due attention paid to variance reduction strategies* New appendix on AMPL in order to better illustrate the optimization models in Chapters 11 and 12* New chapter on binomial and trinomial lattices* Additional treatment of partial differential equations with two space dimensions* Expanded treatment within the chapter on financial theory to provide a more thorough background for engineers not familiar with finance* New coverage of advanced optimization methods and applications later in the textNumerical Methods in Finance and Economics: A MATLAB?-Based Introduction, Second Edition presents basic treatments and more specialized literature, and it also uses algebraic languages, such as AMPL, to connect the pencil-and-paper statement of an optimization model with its solution by a software library. Offering computational practice in both financial engineering and economics fields, this book equips practitioners with the necessary techniques to measure and manage risk.

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Strategic Asset Allocation in Fixed Income Markets: A Matlab based user's guide (The Wiley Finance Series) Review

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(More customer reviews)Quoting the Soviet worker who wrote to The Pravda to condemn "Dr. Zhivago": "I have not read the book, but I deplore it". The table of contents left me puzzled. Term-structure models are dealt with over just 18 pages - this seems to satisfy all the five-star reviewers - after nothing more than fixed-income basics, there's talk of CAPM (is that the proposed approach to fixed-income asset allocation?), a why-these-particular-topics foray into econometrics, and some novice-oriented Matlab content. (GUIs?) I am struggling to see the book as a credible reference on the subject of its title.


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Matlab is used within nearly all investment banks and is a requirement in most quant job ads. There is no other book written for finance practitioners that covers this
Enables readers to implement financial and econometric models in Matlab
All central concepts and theories are illustrated by Matlab implementations which are accompanied by detailed descriptions of the programming steps needed
All concepts and techniques are introduced from a basic level
Chapter 1 introduces Matlab and matrix algebra, it serves to make the reader familiar with the use and basic capabilities if Matlab. The chapter concludes with a walkthrough of a linear regression model, showing how Matlab can be used to solve an example problem analytically and by the use of optimization and simulation techniques
Chapter 2 introduces expected return and risk as central concepts in finance theory using fixed income instruments as examples, the chapter illustrates how risk measures such as standard deviation, Modified duration, VaR, and expected shortfall can be calculated empirically and in closed form
Chapter 3 introduces the concept of diversification and illustrates how the efficient investment frontier can be derived - a Matlab is developed that can be used to calculate a given number of portfolios that lie on an efficient frontier, the chapter also introduces the CAPM
Chapter 4 introduces econometric tools: principle component analysis is presented and used as a prelude to yield-curve factor models. The Nelson-Siegel model is used to introduce the Kalman-Filter as a way to add time-series dynamics to the evolution of yield curves over time, time series models such as Vector Autoregression and regime-switching are also presented
Supported by a website with online resources - www.kennyholm.com where all Matlab programs referred to in the text can be downloaded. The site also contains lecture slides and answers to end of chapter exercises

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Stochastic Simulation and Applications in Finance with MATLAB Programs (The Wiley Finance Series) Review

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(More customer reviews)I've read handful of books on quantitative finance over the years. Most of a few good books that focus on rigorous mathematical treatment often lack practicality. I've been looking for books that offer a rigorous and yet intuitive, practical way to gain an understanding into quantitative finance in general and MonteCarlo simulation in particular. Therefore I often browse Matlab website in search of good books that combine theory and computation. I have found only a good one so far (Higham). But it was written rather for beginners and/or students.
Recently I found this one which is an excellent book for intermediate and advanced users, practitioners and academics alike. The book strikes a good balance between theory and practice; it presents a rigorous and yet intuitive treatment of quantitative finance, from fundamentals of probability theory and random processes, through the foundations of Monte-Carlo method and all the way to real-world applications. The book is up to date with latest research in computational finance. It contains not only simple, pedagogical Matlab programs but also more sophisticate methods such as quadratic resampling, dynamic programming technique of Barraquand and Martineau. The final chapters present the authors' own research, mostly on financial guarantees (credit derivatives) and Value at Risk.
The book presents not only rigorous theory, but also practical, well-designed Matlab programs. The programs contain useful explanatory comments so they are very easy to follow. They can be used as is or adapted for your own purposes. Recently I had to price an exotic executive stock option, and I just chose a Matlab program from the book and modified it. The whole pricing process took just a couple of hours. That is, the programs are good for practitioners working in a high-pressure environment like myself. Academic researchers will also find the Matlab programs useful as the authors generously made codes of their own research on financial guarantees and Value at Risks available. These programs serve as a good starting point for other research projects.
A drawback of this book is that you need Matlab which can be expensive if you don't qualify for an educational licence. However, it does not require other expensive add-ons. I think that interested readers can use open-source Matlab clones like Octave, Scilab etc.

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Stochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance. Building on an integrated approach, it provides a pedagogical treatment of the need-to-know materials in risk management and financial engineering.
The book takes readers through the basic concepts, covering the most recent research and problems in the area, including: the quadratic re-sampling technique, the Least Squared Method, the dynamic programming and Stratified State Aggregation technique to price American options, the extreme value simulation technique to price exotic options and the retrieval of volatility method to estimate Greeks. The authors also present modern term structure of interest rate models and pricing swaptions with the BGM market model, and give a full explanation of corporate securities valuation and credit risk based on the structural approach of Merton. Case studies on financial guarantees illustrate how to implement the simulation techniques in pricing and hedging.

The book also includes an accompanying CD-ROM which provides MATLAB programs for the practical examples and case studies, which will give the reader confidence in using and adapting specific ways to solve problems involving stochastic processes in finance.
"This book provides a very useful set of tools for those who are interested in the simulation method of asset pricing and its implementation with MatLab. It is pitched at just the right level for anyone who seeks to learn about this fascinating area of finance. The collection of specific topics thoughtfully selected by the authors, such as credit risk, loan guarantee and value-at-risk, is an additional nice feature, making it a great source of reference for researchers and practitioners. The book is a valuable contribution to the fast growing area of quantitative finance."
-Tan Wang, Sauder School of Business, UBC
"This book is a good companion to text books on theory, so if you want to get straight to the meat of implementing the classical quantitative finance models here's the answer."
—Paul Wilmott, wilmott.com

"This powerful book is a comprehensive guide for Monte Carlo methods in finance. Every quant knows that one of the biggest issues in finance is to well understand the mathematical framework in order to translate it in programming code. Look at the chapter on Quasi Monte Carlo or the paragraph on variance reduction techniques and you will see that Huu Tue Huynh, Van Son Lai and Issouf Soumaré have done a very good job in order to provide a bridge between the complex mathematics used in finance and the programming implementation. Because it adopts both theoretical and practical point of views with a lot of applications, because it treats about some sophisticated financial problems (like Brownian bridges, jump processes, exotic options pricing or Longstaff-Schwartz methods) and because it is easy to understand, this handbook is valuable for academics, students and financial engineers who want to learn the computational aspects of simulations in finance."
—Thierry Roncalli, Head of Investment Products and Strategies, SGAM Alternative Investments & Professor of Finance, University of Evry

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Business, Economics, and Finance with Matlab, GIS, and Simulation Models Review

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(More customer reviews)As a doctoral student in Finance, I use Matlab a lot for modeling and computation. I had been searching for a reference book that can help me build simulation models using SIMULINK. But the only books available are in specialized fields such as physics or engineering. Patrick Anderson's book attract me immediately by its title. A combination of MATLAB, SIMULATION, GIS and Economics-- that's exactly what I was looking for. It managed to keep up with my high expectation with its highly informative content. The examples given were very well selected and close to application, corresponding Matlab code were neat. It kept surprising me with bonus tricks in using MATLAB(such as getting yahoo stock time seres directly using Matlab, creating XML files), which themselves are well worth the price. I recommend this book to researchers or analysts who want to stand at the frontier of business evaluation, and those who want to exploit the power of Matlab in business forecasting.

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This book takes recent theoretical advances in Finance and Economics and shows how they can be implemented in the real world. It presents tactics for using mathematical and simulation models to solve complex tasks of forecasting income, valuing businesses, predicting retail sales, and evaluating markets and tax and regulatory problems.
Business Economics and Finance with Matlab, GIS, and Simulation Models provides a unique overview of sophisticated business and financial applications. It describes models that have been developed for analysis of retail sales, tax policy, location, economic impact, public policy issues, and other challenges faced by executives, investors, and economists on a daily basis. It also offers groundbreaking insight into the many calculation and modeling tools that can be remotely hosted and run over the Internet, resulting in substantial user benefits and cost savings.
This book is the first to fully explore the capabilities of MATLAB in the field of business economics, and explain how the benefits of sophisticated mathematical models can be provided to users via the Internet, using a thin-client environment. Many techniques directly incorporate geographic information and GIS in a way that was impossible until quite recently. Some techniques, such as fuzzy logic, retail sales, economic and fiscal impact models, and other Matlab and Simulink models, are described for the first time in print in this book. The sections on business income and value break new ground by directly incorporating uncertainty, real option value, and prediction of variables using Ito and jump processes. Using dozens of examples, hundreds of references, and rigorous explanations of both theory and practice, it will become a prized reference for analysts demanding the best techniques.

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Market Risk Analysis Review

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(More customer reviews)This is a very good introduction on the subject of portfolio management. I bought these books as a mathematical engineer because I want to write my thesis about stock options. Everything is clearly explained, they even explain a lot of the easy mathematics you need to succeed in the world of finance. Every book contains a cd which is very handy if you want to calculate an option's price in a minute or something.
In my opinion there is not enough said in the book about options, but then again, it is a book to learn the basics. If you want to become a succesfull options trader, you do need more literature on the forecasting of volatility surfaces and backtesting of technical indicators etc.

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Market Risk Analysis is the most comprehensive, rigorous and detailed resource available on market risk analysis. Written as a series of four interlinked volumes each title is self-contained, although numerous cross-references to other volumes enable readers to obtain further background knowledge and information about financial applications.
Volume I: Quantitative Methods in Finance covers the essential mathematical and financial background for subsequent volumes. Although many readers will already be familiar with this material, few competing texts contain such a complete and pedagogical exposition of all the basic quantitative concepts required for market risk analysis. There are six comprehensive chapters covering all the calculus, linear algebra, probability and statistics, numerical methods and portfolio mathematics that are necessary for market risk analysis. This is an ideal background text for a Masters course in finance.
Volume II: Practical Financial Econometrics provides a detailed understanding of financial econometrics, with applications to asset pricing and fund management as well as to market risk analysis. It covers equity factor models, including a detailed analysis of the Barra model and tracking error, principal component analysis, volatility and correlation, GARCH, cointegration, copulas, Markov switching, quantile regression, discrete choice models, non-linear regression, forecasting and model evaluation.
Volume III: Pricing, Hedging and Trading Financial Instruments has five very long chapters on the pricing, hedging and trading of bonds and swaps, futures and forwards, options and volatility as well detailed descriptions of mapping portfolios of these financial instruments to their risk factors. There are numerous examples, all coded in interactive Excel spreadsheets, including many pricing formulae for exotic options but excluding the calibration of stochastic volatility models, for which Matlab code is provided. The chapters on options and volatility together constitute 50% of the book, the slightly longer chapter on volatility concentrating on the dynamic properties the two volatility surfaces the implied and the local volatility surfaces that accompany an option pricing model, with particular reference to hedging.
Volume IV: Value at Risk Models builds on the three previous volumes to provide by far the most comprehensive and detailed treatment of market VaR models that is currently available in any textbook. The exposition starts at an elementary level but, as in all the other volumes, the pedagogical approach accompanied by numerous interactive Excel spreadsheets allows readers to experience the application of parametric linear, historical simulation and Monte Carlo VaR models to increasingly complex portfolios. Starting with simple positions, after a few chapters we apply value-at-risk models to interest rate sensitive portfolios, large international securities portfolios, commodity futures, path dependent options and much else. This rigorous treatment includes many new results and applications to regulatory and economic capital allocation, measurement of VaR model risk and stress testing.

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Financial Modeling Under Non-Gaussian Distributions (Springer Finance) Review

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(More customer reviews)This book is an outstanding a clear presentation of non-Gaussian financial modeling. In financial markets, the Gaussian curve or bell curve, is not accurate in that most markets are skewed (a predisposition to grow on average, not zero) and fat-tailed (rare events such as market crashes happen more often than a Gaussian curve would suggest). Therefore, non-Gaussian modeling is essential to make money in the market or assess risk. This book goes through all the new techniques of non-Gaussian modeling. It does an exceptional job discussing the GARCH generalized autoregressive conditional heteroskedasticity. This is but a fancy word for fluctuations in volatility over time pretty much dependent on recent fluctuations. It works very well I must say empirically, and has tripled the rationality and profitability of my portfolio - especially one of the versions of the GARCH over the others reviewed - but which one I'd rather not say, for obvious reasons ;) The book is weakest at page 183 or so, with the models and I was rather disappointed with the exclusion of the market crash of the 80s in the empirical analysis - wouldn't rare events be the main reason for improving non-Gaussian modeling? Anyway it's rather poor, but thorough, with additive and multivariate GARCHes but the fault lies with the faultiness of the theories not the authors, at least they're encyclopedic. The book picks up at the end with copulas, and a complete discussion of non-Gaussian option pricing. The review of BSM is appreciated and actually well-done, and a nice reminder of what we are trying to improve on exactly. I think this is a most incredible book, very clearly written, and at times, quite an enjoyable read for such a topic. All it takes is multivariate calculus and basic statistics, but more math ability will make the implications and comments breathtaking at times. I often find myself inspired by a passage or footnote to create a whole subroutine in R or python. I think avoiding Bayesian topics and Monte Carlo was disappointing, but wise in terms of focus. A great book for graduate mathematics in applications of statistics or stochastic calculus, or a good book for modeling fundamentals in economics or business management at the post-graduate level.


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Applied Math for Derivatives: A Non-Quant Guide To The Valuation And Modeling Of Financial Derivatives Review

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(More customer reviews)I agree with the Mr. Phillips about this book merely presenting the basic valuation equations, but I think that is the beauty of this book. Notice the title made a reference to this book beeing intended for 'non-quants'. However this book still provide 'quants' a valuable reference guide when one needs to brush up on the mechanics of a given derivative valuation.
This book is written from a risk-management practitioner point of view and as such it goes in great length in not just showing the different valuation models, which include most of the models in practice, but also the working mechanism of the specific securities market, and the associated exchange and clearing house settlement procedure. The key strong point of this book is that the author wrote every section of the book with conciseness and to the point. Each instrument's characteristics are presented, the associated equations are explained, and the spreadsheet models are shown in detail (included with the accompanying disk). After reading the book one is left with the feeling that finance is really this simple, involving setting the appropiate model to go with the relevant parameters,
One point regarding the editing: it was simply a great pleasure to browse this book. The clean layout of the book, the consistent sequence of presentation of the materials for all the instruments, and the detailed explaination of each of every equation (all the equations all the cells are shown) allows the reader to follow and comprehend the material with ease.
The contents of the books: market mechanism, valuation and model of interest rate forward, foreign exchange forward, equity forward, interest rate swap (the author is really an expert in these types of intruments, showing models of single-rate bond valuation method, simple offset valuation method, zero-coupon yields bootstrapping, zero-coupon yields: forward rate reinvestment, futures strip swap pricing, forward rate offset valuation method, zero-coupon valuation method), cross-currency swaps, equity swaps, equity options, interest rate options, currency options. The disk includes major valuation models of all the derivatives.(most requires just Excel 4.0 version)
Update: Since this book was published over a year ago, many other fine derivatives books have been published. However its straightforward simplicity still makes it a valuable part of a risk manager's personal library. One minor objection even at [...]its list price it is still priced a tad [...] for an introductory/intermediate level textbook. Anyone more quantitative-oriented, might want to check Cuthbertson's Financial Engineering and Risk Management. Comes with software and real life application examples.

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A handy guide/reference for investors, analysts, and students, Mathematics for Derivatives provides an integrated approach to the valuation of financial derivative instruments for a wide range of asset classes. Featuring a user-friendly format, it was designed to be used as both a step-by-step guide to derivative pricing for beginners, and a handy quick-reference for experienced market practitioners in need of a refresher on the intricacies of a specific instrument. Offering comprehensive coverage of derivative instruments, simple valuation methods, and many detailed examples, this book is sure to be warmly received by professional investors, fund managers, brokers, risk managers, analysts, financial software developers, and all who need a working knowledge of the mathematical techniques used in the derivatives industry.John Martin (Australia) has worked, taught and published extensively in the areas of treasury, derivatives and financial risk management. He was closely involved in the development of the derivatives industry in Australia in roles varying from market trader, risk manager, regulator and educator. He is a Partner at PricewaterhouseCoopers in Australia.

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QUANTITATIVE ANALYSIS, DERIVATIVES MODELING, AND TRADING STRATEGIES: IN THE PRESENCE OF COUNTERPARTY CREDIT RISK FOR THE FIXED-INCOME MARKET Review

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(More customer reviews)I am a junior quant, so let us not comparing with guru. Among the books I read, my opinion is Wilmott series more on maths, Baxter for good understandings until short rates, Jessica wide but not so deep concepts on models/methods, Rebonato's books are detailed, but I found the detailed explanations are not clear to me, Brigo&Mercurio's book is nice. A common drawback of all these books is they didnot talk much on practitioners' intuitions.
This QA+DM+TS book combines math, intuitions, traders' views. And they are explained in such a clear way. All the models are alive. Really hope I could have known about the book and read it in full before my interviews. Sigh...

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Mathematical Finance: Theory, Modeling, Implementation Review

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(More customer reviews)Disclaimer: As you can see from Amazon RealName (TM), I am the author of the book. The editorial review provided on the back of the book and reproduced on amazon was written by the publisher. However, that editorial review does not provide as much information about the book as I think is necessary. This review hopefully provides you with a more detailed description of the contents and objectives of the book, to help you finding the right book for your needs. [...]
The book's main objective is to provide an intuition for the theoretical concepts relevant for derivative pricing and to bridge from the more academic concepts (filtration, random variable, stochastic process) to their application in industry, most notably modeling, calibration and object oriented implementation. It comes with extensive additional material to further explore the key concepts. See the book's home page at christian-fries.de/finmath/book
The book starts like a textbook giving an introduction to probability theory and stochastic processes. However, instead of repeating "Definition-Theorem-Proof" the book often leaves out the proof and adds two special sections: "Motivation" and "Interpretation" (before and after a definition or theorem). The first part makes you acquainted with the mathematical theory and provides the intuition for the fundamental building blocks like random variable, brownian motion, drift and volatility, Ito process, measures, change of measure and numéraire, etc.
In the second part, first applications are, of course, the Black-Scholes model for a single asset. As an excursion important concepts like implied volatility, hedging and the greeks are presented. The results and graphs of these applications may be explored interactively in Java applets on associated web pages.
The third part introduces interest rates, interest rate products and further analytical pricing models. At first, this might come as an arbitrary choice of a specific asset class, namely to focus on interest rates in contrast then equity, foreign exchange (fx), or credit derivatives. However, there is a motivation on why interest rates are a natural choice if one wants to move to more complex derivatives like they have become popular recently: Derivatives feature payments or cash-flows (settlements) at different times, and interest rates are one way to describe the value of future payouts. Mathematically speaking, interest rate products (like bonds or money market accounts) are a natural choice for a numéraire. So interest rates are part of any model (e.g. the black-scholes model for equity and foreign exchange) and considering stochastic interest rates will make these models into hybrid interest rate models.
Before discussing interest rates models (part V) or hybrid models (part VI), the part IV of the book gives a treatment of the numerical implementation of such models. It focuses on Monte-Carlo simulations and their object oriented implementation. Monte-Carlo simulation is one of the most powerful tools in (numerical) derivative pricing. It is also a straight forward approach to implement models, making as few assumption as possible (for example: finite differences, like PDEs and trees are limited to low(er) dimensions). Despite its ubiquitous application, Monte-Carlo simulation brings several disadvantages: a) It is sometimes slower. Given the performance of todays computers, this disadvantage is becoming less important. b) Bermudan options are hard to price. This is solved in Chapter 15. Path-dependent bermudan options are even harder. This is solved in Chapter 16. c) Sensitivities are unstable. This is solved in Chapter 17 and 18.
Part V introduces bigger models, like the LIBOR Market Model, the classical Short Rate Models, Heath-Jarrow-Morton Framework, Cheyette Model and Markov Functional Models. This part focuses a bit on the LIBOR Market Model as it is our workhorse. The calibration of the LIBOR Market Model is discussed (e.g. the calibration to swaption volatility and swap rate covariance) and hints for fast, object oriented implementations are given. Object oriented designs are given in UML diagrams. In "Excursions" concepts like mean-reversion, instantaneous and terminal correlation, multi-factor model, etc. are discussed and illustrated. This part will both endow you with a solid intuition of important model aspects as well as the ability to actually implement such model.
Part VI builds upon the models presented in part V to introduce model extensions like credit spread (credit default) or hybrid models. Examples for hybrid-models are equity-interest rate hybrid model, fx-interest rate hybrid model, multi-currency model. The equity-interest rate hybrid model is essentially a Black-Scholes model (as it was discussed in the second part of the book) with stochastic interest rate modeled by a LIBOR market model (as it was discussed in the fifth part of the book). Since the numéraire is an interest rate product, a Black-Scholes model with stochastic interest rates becomes an interest rate model with an extension.
Part VII gives a short introduction to object oriented implementation.

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Simulation and Optimization in Finance + Website: Modeling with MATLAB, @Risk, or VBA (Frank J. Fabozzi Series) Review

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(More customer reviews)Prof.Pachamanova has written one of the best introductions to Simulation and Optimization methods in finance. This book provides a strong theoretical foundation and the website provides a lot of cases and useful hands-on exercises to apply and understand the concepts explained in the book. This book is highly recommended for business and engineering students who are interested in a career in the quantitative finance industry. This book is also recommended to new entrants to the quant finance industry and to financial practitioners who primarily use Excel for quantitative modeling but are interested in building more rigorous models using VBA, @RISK and MATLAB.
This book has the optimal combination of theory and practice. It starts out with a through introduction to statistics, finance and optimization concepts. In the second part, the book describes portfolio optimization theory and applications in equity and fixed income markets. The third part focuses on asset pricing models discussing classical and dynamic models. The fourth section mainly focuses on derivative pricing and provides a very good introduction to Monte-Carlo simulation methods. Topics of current interest such as pricing MBS products are also described in this section. Part five focuses on capital budget decisions and has a very good introduction to real options. The Software hints in each chapter and the supplementary materials on the website help students and practitioners to immediately try out examples and fortify their knowledge.
Prof.Pachamanova's didactic approach and the vast coverage of topics makes this book a must have for new quantitative analysts, business students and engineers interested in a career in finance. As a Financial Modeling consultant who works at MathWorks (the maker of MATLAB), I get a lot of questions on recommendations for books to apply financial theory using computational tools. This book is a gem and would makes great addition to your quantitative investing library.
Full Disclosure: I took a Statistics class with Prof.Pachamanova during my MBA program at Babson College

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Optimal Portfolio Modeling, CD-ROM includes Models Using Excel and R: Models to Maximize Returns and Control Risk in Excel and R (Wiley Trading) Review

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(More customer reviews)This book is an excellent introduction to the world of portfolio management and managing risk and return in general. The author's approach is from a statistical viewpoint but with minimal math. The book covers such areas as market micro structure and the distribution of price changes. It also debunks some myths about how effective stop losses are. That part was very interesting and clearly identifies when to use stop losses and when not to do so.
The treatment of how to maximize Sharpe Ratio was very clear and thorough but still accessible to the layman. For those who do not know any Excel at all this book may need to be supplemented with an introductory Excel book. But all of the more advanced Excel features are fully explained and very clear. Examples are presented in both Excel and the language R. The examples are also available on the CD that accompanies the text. For novices in the statistical language R, a full introduction is provided as an appendix. This was very helpful. It is almost like getting two books for the price of one.
The discussion of robust random portfolio modeling was very advanced and yet treated at an introductory level. The dual discussion of the log normal distribution along with the empirical distribution of real fat tailed markets was very refreshing. The book also espouses a new log log utility model that is quite innovative. All in all, it is an innovative book that is well written and easy to understand.


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Optimal Portfolio Modeling is an easily accessible introduction to portfolio modeling for those who prefer an intuitive approach to this discipline. While early chapters provide engaging insights on the statistical properties of markets, this book quickly moves on to illustrate invaluable trading and risk control models based on popular programs such as Excel and the statistical modeling language R. This reliable resource presents modeling formulas that will allow you to effectively maximize the performance, minimize the drawdown, and manage the risk of your portfolio.

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Modeling Risk, + DVD: Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, and Portfolio Optimization (Wiley Finance) Review

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(More customer reviews)Dr Mun's books are always very insightful with lots of practical examples and tools. A very good book for the professional that needs an in depth understanding of risk management. The book provides many analytical tools to properly assess and mitigate risks!


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An updated guide to risk analysis and modeling
Although risk was once seen as something that was both unpredictable and uncontrollable, the evolution of risk analysis tools and theories has changed the way we look at this important business element. In the Second Edition of Analyzing and Modeling Risk, expert Dr. Johnathan Mun provides up-to-date coverage of risk analysis as it is applied within the realms of business risk analysis and offers an intuitive feel of what risk looks like, as well as the different ways of quantifying it.
This Second Edition provides professionals in all industries a more comprehensive guide on such key concepts as risk and return, the fundamentals of model building, Monte Carlo simulation, forecasting, time-series and regression analysis, optimization, real options, and more.
Includes new examples, questions, and exercises as well as updates using Excel 2007
Book supported by author's proprietary risk analysis software found on the companion CD-ROM
Offers both a qualitative and quantitative description of risk

Filled with in-depth insights and practical advice, this reliable resource covers all of the essential tools and techniques that risk managers need to successfully conduct risk analysis.
Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

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