- Gebundene Ausgabe: 588 Seiten
- Verlag: Springer; Auflage: 2nd ed. 2014 (8. Januar 2014)
- Sprache: Englisch
- ISBN-10: 1461487870
- ISBN-13: 978-1461487876
- Größe und/oder Gewicht: 17,8 x 3,3 x 25,4 cm
- Durchschnittliche Kundenbewertung: 1 Kundenrezension
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- Komplettes Inhaltsverzeichnis ansehen
Statistical Analysis of Financial Data in R (Springer Texts in Statistics) (Englisch) Gebundene Ausgabe – 8. Januar 2014
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Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This book fills this gap by addressing some of the most challenging issues facing any financial engineer. It shows how sophisticated mathematics and modern statistical techniques can be used in concrete financial problems.
Concerns of risk management are addressed by the control of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Data description techniques such as principal component analysis (PCA), smoothing, and regression are applied to the construction of yield and forward curve. Nonparametric estimation and nonlinear filtering are used for option pricing and earnings prediction.
The book is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. Because it was designed as a teaching vehicle, it is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the computing environment of R. They illustrate problems occurring in the commodity and energy markets, the fixed income markets as well as the equity markets, and even some new emerging markets like the weather markets.
The book can help quantitative analysts by guiding them through the details of statistical model estimation and implementation. It will also be of interest to researchers wishing to manipulate financial data, implement abstract concepts, and test mathematical theories, especially by addressing practical issues that are often neglected in the presentation of the theory.
Über den Autor und weitere Mitwirkende
René Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas.
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I had expected the book to start off with Box-Jenkins-style time series analysis (the familiar AR-to-GARCH road, if you will), but found time series relegated to the book's last third. Instead, "Statistical analysis of financial data in R" leads with (return) distributions, and while I do see the necessity of introducing distributions in a statistics textbook for undergrads and master's-level students, the subsequent foray into extreme-value theory struck me as catering to a niche interest. (Catering to the same interest is "Financial risk modeling and portfolio optimization with R" by Pfaff).
The book's middle third is devoted to regression, and the complaints here are (a) the generic nature of the discussion: there's nothing finance-specific about regression, (b) the unfulfilled promise (expressed in the preface) of a more "modern" approach focusing on robust and non-parametric methods: LOESS and projection-pursuit regression tick the box, but do not impress. (Discussion of Kalman filter and HMM models in the time-series part, on the other hand, does, even if it is quite brief).
Overall, I feel that the book's content was shaped by the author's preferences to a greater extent than a qualified editor would allow. Another aspect of the alleged "indulgence" is the book's (actually modest) usage of R, which sticks to the author's custom R package, and makes little use of the plentiful resources out there, be it financial calculations or auxiliary data manipulation. This insularity could be avoided if R-literate colleagues or graduate assistants were involved; typos sprinkled through the text - and what looks like a finance-theory blunder on page 55: is the equity-premium puzzle really about the difficulty of estimating the average return vs. estimating the volatility? - tell me that those were not even invited to read the manuscript.
I reiterate my positive overall impression, but advise prospective buyers to also take a look at "Statistics and data analysis for financial engineering" by David Ruppert.
I'm sitting here trying to do a problem set, and it is literally impossible because half the equations have typos in them. I don't know which formulas to trust, and trying to derive the equations they obtain is incredibly difficult when the original equations are wrong.
The book claims there were four editors, but I'm going to have to assume that too is a typo because even one editor could have caught half these mistakes. Maybe these editors weren't math people, so they didn't realize the equation were wrong, but there are still grammatical and spelling errors everywhere.
Fix these problems and I'd be glad to give a higher review because the material is explained relatively well, but the typos and errors make this book useless in its present state.
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