Product Description

The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory.

Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications.

Product Details

• Amazon Sales Rank: #238278 in Books
• Published on: 1996-12-09
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 632 pages

Review
Written by the "A" team of financial empiricism, it is a long awaited book. It covers many topics one could only usually find couched in the technical jargon of research papers, presented in this volume with pedagogical intentions. The language, while remaining technical, is quite accessible. It can be effortlessly read by scientific traders with standard knowledge of statistical methods. . . . This book should be made mandatory reading in research departments. -- Review

Review
The definitive work explaining this complex but important field of academic endeavor. Oh, and by the way, it's not just academic. The big question that financial econometircs addresses is: What can you learn about the future from the financial data available from the past? This broad issue can be specified in many different ways, and all the important ones are discussed in the book. . . . The vast literature on all the topics examined is assessed, rendered coherent, and then analysed by three men who themselves have made significant advances in the field.
(Ruben Lee London Financial Market )

This book is sophisticated, yet accessible; full of details, yet intriguing. . . . Instructors will appreciate the attempt to make each chapter as self contained as possible which leaves them free to choose specified sequences of topics. Professionals will be pleased with the quick and authoritative introductions to important areas of Finance. . . . [A] well written introduction (indeed, something more) to Financial Econometrics. It is alert, explicit and articulate about assumptions. . . a splendid offering. . . .
(Maurizio Tiso Review of Financial Studies )

Written by the "A" team of financial empiricism, it is a long awaited book. It covers many topics one could only usually find couched in the technical jargon of research papers, presented in this volume with pedagogical intentions. The language, while remaining technical, is quite accessible. It can be effortlessly read by scientific traders with standard knowledge of statistical methods. . . . This book should be made mandatory reading in research departments.
(Derivative Strategies )

From the Publisher
The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduatelevel textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory.

Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing stateofthe art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications.

Customer Reviews

Spend your money on something better
This book seems to have written to cash in on the fame of the authors and the stampede in academia and industry towards financial econometrics.

The book already assumes you are proficent in basic and advanced econometrics, derivatives pricing, fixed income, microstructure, neural networks etc. If you already familiar with those fields, why do you need this book? For example, Chapter 10 on Fixed Income Securities covers a grand total of 28 pages beginning with "Basic Concepts" and ending with "Yield Spreads and Interest Rate forecasts". Meanwhile there are whole tomes devoted to every one of those sections in Chapter 10. Nonparameteric Estimation merits a grand total of 9 pages and Neural networks merits 7 pages in Chapter 12.

The chapter on Microstructure, virtue of the book being published in 1997 is thoroughly dated. Even for its 1997 publication the chapter is thoroughly lacking. It is neither a survey nor a exposition of theory or practial uses of microstructure theory. Today there are excellent theoretical and practical books devoted to every topic covered in this book.

Save your money for one of those.

An excellent text for the advanced reader
This is a concise treatment of major foundation topics in financial economics. Although my interest is in monetary economics and macro, I finally have a book I will keep and use on financial economics. It closely blends the insight and "wisdom" behind the various theories with parsimonious amounts of math. Careful, patient reading and a comfortable grasp of econometrics is required but will be rewarded. Notation changes were a bit of a problem, though the authors address this issue early on. The end of chapter questions are good but it would've helped to have answers. Overall, it is intuitive "page turner" material.

CML: An Unnecessary Addition to a Saturated Literature
I was also skeptical of the negative reviews surrounding this book ("CML"). However after buying and reading this book, I now believe they had merit.

Simply stated, this book does not cater to its readers. If you have the prerequisites that the authors demand, then this book is comprehensive but ultimately below what ought to challenge you. And if you don't, then I guarantee you will be very lost. Unlike many similar volumes, CML is not self-contained (nor does it claim to be). And unlike many books that build a self-contained "model" of asset pricing dynamics, CML is full of literature-specific jargon and inconsistent notation. In fact much of this notation changes intrachapter.

Suppose you are a reader at the level CML insist their readers be. Then all the better to spend more time understanding Duffie's "Dynamic Asset Pricing," or Cochrane's veritable tour-de-force, "Asset Pricing." Both books are more contemporary and also at a better level for the readers CLM had in mind.

If you don't have the requisite knowledge, please ignore CML and try Luenenberger and Casella/Berger, as well as Greene for econometric-specific stats, Hamilton for time-series. You will not regret these purchases.

CML claims to fill a gaping hole in the secondary literature. But in reality, CML sits right in the middle of two types of readers, and caters effectively to none.