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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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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Heavy-Tail Phenomena: Probabilistic and Statistical Modeling (Springer Series in Operations Research and Financial Engineering) Review

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(More customer reviews)Sid Resnick taught me stochastic processes when I was a graduate student at Stanford. He is an exceptional lecturer who really stimulates the students. I don't think I ever had a boring lecture from him even though probability theory and stochastic processes can at times be very dry subjects.
Over the many years since then Sid has moved on and spent many fruitful years at Colorado State and now Cornell. In addition to his many excellent papers and his fine collaborative work with Richard Davis, he has written a large number of very interesting and thought provoking texts on extreme value theory, stochastic processes and probability theory. I have a deep appreciation for his contributions to the theory of extremes as that has also been one of my research areas and was the topic of my Ph.D. dissertation. This is the second outstanding book Resnick has written on extremes. This one has more of a modelling flavor to it with an eye toward financial applications. It seems these days that much of the research in time series modelling and stochastic processes is motivated by applications in finance. This is certainly also the case with extreme value models as can be seen by the many fine books on extremes that have appeared recently.
This book shows the theory and applications of models for heavy-tailed distributions. Resnick makes a very good point about insurance claim cost. This was certainly a phenomena I had to deal with when modeling workers compensation insurance claims at Risk Data Corporation. It is also interesting to see coverage about what is needed to consistently estimate extreme values by bootstrapping.
Time series models that deal with heavy-tailed distributions are also mentioned.

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This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models.Prerequisites for the reader include a prior course in stochastic processes and probability, some statistical background, some familiarity with time series analysis, and ability to use a statistics package. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.

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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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This book examines non-Gaussian distributions. It addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. The book is written for non-mathematicians who want to model financial market prices so the emphasis throughout is on practice. There are abundant empirical illustrations of the models and techniques described, many of which could be equally applied to other financial time series.

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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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A balanced introduction to the theoretical foundations and real-world applications of mathematical finance
The ever-growing use of derivative products makes it essential for financial industry practitioners to have a solid understanding of derivative pricing. To cope with the growing complexity, narrowing margins, and shortening life-cycle of the individual derivative product, an efficient, yet modular, implementation of the pricing algorithms is necessary. Mathematical Finance is the first book to harmonize the theory, modeling, and implementation of today's most prevalent pricing models under one convenient cover. Building a bridge from academia to practice, this self-contained text applies theoretical concepts to real-world examples and introduces state-of-the-art, object-oriented programming techniques that equip the reader with the conceptual and illustrative tools needed to understand and develop successful derivative pricing models.
Utilizing almost twenty years of academic and industry experience, the author discusses the mathematical concepts that are the foundation of commonly used derivative pricing models, and insightful Motivation and Interpretation sections for each concept are presented to further illustrate the relationship between theory and practice. In-depth coverage of the common characteristics found amongst successful pricing models are provided in addition to key techniques and tips for the construction of these models. The opportunity to interactively explore the book's principal ideas and methodologies is made possible via a related Web site that features interactive Java experiments and exercises.
While a high standard of mathematical precision is retained, Mathematical Finance emphasizes practical motivations, interpretations, and results and is an excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level. It also serves as a valuable reference for professionals in the banking, insurance, and asset management industries.

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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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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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An introduction to the theory and practice of financial simulation and optimization
In recent years, there has been a notable increase in the use of simulation and optimization methods in the financial industry. Applications include portfolio allocation, risk management, pricing, and capital budgeting under uncertainty.
This accessible guide provides an introduction to the simulation and optimization techniques most widely used in finance, while at the same time offering background on the financial concepts in these applications. In addition, it clarifies difficult concepts in traditional models of uncertainty in finance, and teaches you how to build models with software. It does this by reviewing current simulation and optimization methodology-along with available software-and proceeds with portfolio risk management, modeling of random processes, pricing of financial derivatives, and real options applications.
Contains a unique combination of finance theory and rigorous mathematical modeling emphasizing a hands-on approach through implementation with software
Highlights not only classical applications, but also more recent developments, such as pricing of mortgage-backed securities
Includes models and code in both spreadsheet-based software (@RISK, Solver, Evolver, VBA) and mathematical modeling software (MATLAB)

Filled with in-depth insights and practical advice, Simulation and Optimization Modeling in Finance offers essential guidance on some of the most important topics in financial management.

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