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Financial Modeling Under Non-Gaussian Distributions (Springer Finance), by Eric Jondeau, Ser-Huang Poon, Michael Rockinger
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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.
- Sales Rank: #1503374 in Books
- Published on: 2010-09-02
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.27" w x 6.13" l, 1.71 pounds
- Binding: Paperback
- 541 pages
From the Back Cover
Practitioners and researchers who have handled financial market data know that asset returns do not behave according to the bell-shaped curve, associated with the Gaussian or normal distribution. Indeed, the use of Gaussian models when the asset return distributions are not normal could lead to a wrong choice of portfolio, the underestimation of extreme losses or mispriced derivative products. Consequently, non-Gaussian models and models based on processes with jumps are gaining popularity among financial market practitioners.
Non-Gaussian distributions are the key theme of this book which addresses the causes and consequences of non-normality and time dependency in both asset returns and option prices. One of the main aims is to bridge the gap between the theoretical developments and the practical implementations of what many users and researchers perceive as "sophisticated" models or black boxes. 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, such as exchange and interest rates. The authors have taken care to make the material accessible to anyone with a basic knowledge of statistics, calculus and probability, while at the same time preserving the mathematical rigor and complexity of the original models.
This book will be an essential reference for practitioners in the finance industry, especially those responsible for managing portfolios and monitoring financial risk, but it will also be useful for mathematicians who want to know more about how their mathematical tools are applied in finance, and as a text for advanced courses in empirical finance; financial econometrics and financial derivatives
Review
From the reviews:
"Financial Modeling Under Non-Gaussian Distributions … is thus very welcome as it provides an accessible and easy-to-understand treatment of a broad range of topics, including core material to more advanced techniques on the subject of capturing non-Gaussian properties in the distributions of asset returns. … Financial Modeling Under Non-Gaussian Distributions is a very accessible textbook that covers a wide range of topics. … The authors define their target readers as specialized master and Ph.D. students, as well as financial industry practitioners." (Stephan Suess, Financial Markets and Portfolio Management, Vol. 22, 2008)
"This book is written for non-mathematicians who want to model financial market prices. ... It targets practioners in the financial industry. It is suitable for use as core text for students in empirical finance, financial econometrics and financial derivatives. It is useful for mathematician who want to know more about their mathematical tools are applied in finance." (Klaus Ehemann, Zentralblatt MATH, Vol. 1138 (16), 2008)
Most helpful customer reviews
11 of 14 people found the following review helpful.
A most intelligent and clear book
By Brian D. Babiak
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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