Robust Portfolio Optimization and Management (Frank J Fabozzi Series) Review

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(More customer reviews)Quick fact:
1) Highly recommand this book to serious Quants.
2) Graduate lever math is required for serious reader.
3) Good reference book and good for self-study
4) Well written, easy read.
5) worth the money.
The field of quantitative techniques have developed so much in the last 10 years, but almost no book cover enough serious topics about these new directions. I had already learn a bit of the robust techniques while working, including robust estimates, robust portfolio construction, error control, bayesian estimates and others. But those are all picked up in pieces, at different times, and with much research efforts. So you can imagine my delight to see a book that covers a lot of the pieces concisely.
This book itself is very well written, occasionally misspelled math labels are easily corrected by more math inclined reader, and will not interfare with casual reading. Like many of Fabozzi books, overall organization is slightly loose, so that you can start any chapter in the book and still get pretty much decent view about that subject. But better written for quants than some of Fabozzi's early books (which are mainly used as reference books)
what is missing in this book?
just one: sometimes, reference papers or books are given even though a little more details would save serious reader a lot more time. Yes, I know, those are advance topics, still would like to see them as a serious reader. Maybe as appendix for relevent chapers.
Over all, worth every penny of it.


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Praise for Robust Portfolio Optimization and Management"In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction."--Mark Kritzman, President and CEO, Windham Capital Management, LLC"The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike."--John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University

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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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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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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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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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