Fat-Tailed and Skewed Asset Return Distributions: Implications for Risk Management, Portfolio Selection, and Option Pricing Hardcover – Jul 22 2005
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"On the whole a valuable attempt to continue the work of Mandlebrot and others, to break the habit of treating the normal distribution curve as. . . normal." -- HedgeWorld News
"This book is well-written by knowledgeable authors and provides readers with an excellent overview of where fat-tailed or skewed distributions may be needed. The book unfolds in a clear and easy-to-read way, and I would definitely recommend this as an excellent introductory text." -- Financial Engineering News, June 30, 2006
From the Back Cover
Fat-Tailed and Skewed Asset Return Distributions
While mainstream financial theories and applications assume that asset returns are normally distributed, the overwhelming empirical evidence shows otherwise. Yet many professionals fail to appreciate the highly statistical models that take this empirical evidence into consideration.
Svetlozar Rachev, Christian Menn, and Frank Fabozzi understand this dilemma, and in Fat-Tailed and Skewed Asset Return Distributions, they offer you a less technical look at how portfolio selection, risk management, and option pricing modeling should and can be undertaken when the assumption of a non-normal distribution for asset returns is violated.
Topics covered in this comprehensive book include:
- An extensive discussion of probability distributions used in finance
- Estimating probability distributions
- The basics of stochastic processes
- Portfolio selection and alternative risk measures
- Market, credit, and operational risk measurement
- Black-Scholes option pricing model and its extensions when the model's assumptions are modified to meet the empirical distributional evidence and tests
- And much more
Fat-Tailed and Skewed Asset Return Distributions provides a bridge between the highly technical theory of statistical distributional analysis, stochastic processes, and econometrics of financial returns and real-world risk management and investments.See all Product Description
Most Helpful Customer Reviews on Amazon.com (beta)
The "book" is a series of shallow, disjointed chapters that just touch on the important topics. It superficially skims a wide range of issues so that the reader can be a term-dropping jack-of-all-trades but master of none.
I was partially lulled by its availability as a .pdf, which is convenient, and its title and purported thesis of heavy-tailed modeling, which really is a very important thesis but is just a red herring here: the book provides very superficial treatment of this concept throughout, without really building a solid methodological case for it (even though its true, which is why its clever and deceptive marketing, rather than a scholarly OR useful practitioner work). It provides no new insight generally, and the only new insight to me was that I am beginning to see what passes as a typical of Fabozzi publication. I'm angry I wasted the $$.
Now, READ THE PREFACE: page xii "We must admit our intent at the outset was to provide a NON-TECHNICAL treatment of the topic."
If you can't understand who this book is intended for, are you qualified to write a review? To dismiss this as a book for "name droppers" reflects an arrogant misunderstanding. Everyone has to start somewhere on the learning curve.
In terms of its stated aim, this book does an excellent job. There are hundreds of thousands of investment "professionals" who have never heard of stable Paretian distributions or copulas, who would benefit from education. And, yes it is printed on cheap paper. But that makes it very light & easy to carry round! Is it overpriced? Of course - nothing new there. But savvy buyers don't pay full price anyway.
But most of all, this book does not explain in required detail the distributions nor generating functions. This is for someone who wants to learn some term-dropping without profound insight.
RECOMMENDATION: TO LEARN, BUT ANOTHER BOOK. FABOZZI PUBLICATIONS ARE ALWAYS THE SAME, SUPERFLUOUS AND EXPENSIVE.
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