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Statistical Analysis of Financial Data in R (Springer Texts in Statistics)
 
 

Statistical Analysis of Financial Data in R (Springer Texts in Statistics) [Kindle Edition]

René Carmona

Kindle-Preis: EUR 47,35 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

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Kurzbeschreibung

Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how sophisticated mathematics and modern statistical techniques can be used in the solutions of concrete financial problems. Concerns of risk management are addressed by the study of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Principal component analysis (PCA), smoothing, and regression techniques are applied to the construction of yield and forward curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Nonlinear filtering is applied to Monte Carlo simulations, option pricing and earnings prediction. This textbook is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. It is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the R computing environment. They illustrate problems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the library Rsafd developed for the purpose of the text. The book should help quantitative analysts learn and implement advanced statistical concepts. Also, it will be valuable for researchers wishing to gain experience with financial data, implement and test mathematical theories, and address practical issues that are often ignored or underestimated in academic curricula.

This is the new, fully-revised edition to the book Statistical Analysis of Financial Data in S-Plus.

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.

Buchrückseite

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.


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Amazon.com: 4.5 von 5 Sternen  2 Rezensionen
2 von 2 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Mixed feelings 26. Februar 2014
Von Dimitri Shvorob - Veröffentlicht auf Amazon.com
Format:Kindle Edition
There is no denying the book's quality and value as a textbook - note the end-of-chapter exercises - and I will add originality to the list of its virtues, and then wonder if a more conventional, consultative approach would actually have worked better.

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.
0 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Where is the Rsafd download for version 3.01? 31. Dezember 2013
Von Amazon Customer - Veröffentlicht auf Amazon.com
Format:Kindle Edition
This is clearly a great book.

Professor Carmona's body of work on the internet is superior, and this book is a very nice addition to the Fin Stats in R catalog.
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