Benninga's "Financial Modeling"

Financial Modeling, 3rd Edition, by Simon Benninga, could easily be retitled "Quant Finance 101." It's indispensable as a first text on quant finance. Let me sing its praises.

The idea of the book is to build Excel-based models in the areas of basic finance, option pricing, portfolio theory, and bond pricing. The author uses VBA and other advanced features of Excel. In this connection roughly the last 300 pages of this 1100-page book are devoted to explaining the advanced features of Excel: data tables, matrix operations, functions, array functions, arrays, and VBA (including types and loops). In brief, all the Excel a quant would probably ever need.

The book starts with a discussion of rates of return, NPV, the Gordon dividend model, calculating the weighted average cost of capital, financial statement modeling, bank valuation, and leasing. This is the first part.

The second part deals with portfolios: CAPM, minimum variance portfolios, Value at Risk, event studies, and the Black-Litterman model (this is the clearest explanation I've seen of Black-Litterman so far, and the calculations performed really drive the point home: this alone justifies the price of the book).

The third part deals with options. Note that the treatment doesn't develop the stochastic calculus required for understanding Black-Scholes: merely how to implement various pricing models. In this connection the author introduces both the binomial model, the Black-Scholes formula model, the Greeks, and (naive) Monte Carlo methods for pricing Asian and Barrier options.

And finally, the fourth part deals with calculating bond duration and convexity, immunization strategies, calculating term structure, and default adjustment.

The third edition -- which I have in my hands -- has just come out. As is usual with such books these days, it comes with its own CD (with the Excel models, macros, and VBA code). In my humble opinion this is the one book all quants and finance MBAs should have read and assimilated cover-to-cover. It is Quant Finance 101 and will serve as a sturdy and reliable foundation for more advanced and/or theoretical work.

Dash's "Quantitative Finance and Risk Management"

First of all, with regard to Quantitative Finance and Risk Management: A Physicist's Approach, one can inspect portions of the book here. And a forum discussion of the book can be found here at Wilmott.

As I've said elsewhere, the book is somewhere between a high-level survey and a monograph. It is not encyclopedic: it cover certain topics only, and those too in a manner not suited too those who haven't already had prior exposure to them. This is a book written by a quant for other pros, and for some topics those pros should ideally have a Ph.D. in the hard sciences (Dash discusses ideas such as path integrals and Green's functions). With these qualifications, the book can be recommended, and recommended strongly: it provides a terse and sophisticated coverage of the topics it deals with and that a quant will need in his day-to-day work. Let's take a quick glance at the contents.

The 800 pages are divided into six parts: Introduction, Risk Lab ("nuts and bolts of risk management"), Exotics, Quantitative Risk Management, Path Integrals and Green's Functions, and the Macro-Micro Model. Each part contains several chapters; altogether there are fifty-two chapters. The chapter titles can be seen in the link provided at the top of this post. Each chapter is given a "tech. index" varying from 0 to 10 (based on how demanding it is). According to the author, an 8,9, or 10 requires Ph.D+ sophistication; and anything less than or equal to 5 can be taught to MBA students. I think this is overly optimistic. Even if it were used for such a purpose, such students would need supplementary reading material to fill in the holes the author has no space or patience in filling. And to reiterate an earlier point, I'm not convinced this book is the best place to learn such material. There are better and more systematic books to learn interest-rate swaps, interest-rate swaptions, and VaR from. Where the book shines is its discussion of advanced material, for example, inter alia, that pertaining to barrier options, average-rate options, equity volatility skew, and correlation matrix formalism.

In summary, if one has had top-notch schooling and is working as a quant in a hub such as London or New York, the book will be useful, maybe even extremely useful. It might also be used for Ph.D. and practitioner seminars at leading quant schools. Everyone else should probably leave the book alone.

Meucci's "Risk and Asset Allocation"

Risk and Asset Allocation, by Attilio Meucci, was published by Springer in 2005. The book has been favorably reviewed by -- among others -- Wilmott, Carr, Littermann, and Duffy. I find it difficult to agree with these learned gentlemen and wonder whether they've tried reading the book or whether they just skimmed a few pages. My honest opinion is that this book is worthless either as text or as monograph.

There are mathematics texts and monographs that are difficult -- just about anything by Serre is difficult, for example, partly because his style is extremely compact and partly because he's dealing with inherently abstruse concepts. But Meucci's style is obscure and obfuscating for no good reason. What he's trying to convey could be done far better with easier terminology and clear worked examples. Math books -- and treatments trying to masquerade as such -- should move from the simple to the complex, and each time an abstract idea is presented, try to give both an intuitive explanation and at least one fully worked example. Meucci hasn't the foggiest idea of how to do this. For just one example in mathematics, look at Rotman's Modern Abstract Algebra for pedagogic clarity in exposition. Alternatively, look at Shreve's Stochastic Calculus in Finance.

There are far better books on asset allocation available. I'll discuss these in due course.

Intro to computational finance with MatLab

One book which I've used and can heartily recommend is Higham's An Introduction to Financial Option Valuation: Mathematics, Stochastics and Computation, published by Cambridge (2004). This book has been written for a leisurely but stimulating one-semester course in computational finance at the undergrad level; the code is in MatLab.

The first thing to commend this book is that little background is assumed either in terms of mathematics or programming. Any American student who has taken three semesters of calculus should be able to follow the book (though it would be helpful if one came with a stronger math and programming background). And the MatLab code to implement the various algorithms is given in full.

The second thing to commend this book is how far it manages to go assuming such a threadbare background: in 24 chapters spanning 270 pages, the author covers, inter alia, Monte Carlo, variance reduction, the Binomial method, the Black-Scholes PDE, hedging, the Greeks, implied volatility, exotic options, and finite difference methods.

Students in MBA programs should also find the book useful and those in MFE programs might want it as a supplementary text (it's too elementary to serve as the main text in an MFE computational math course, for which something like Brandimarte's Numerical Methods in Finance and Economics might be more appropriate).