Statistics and Data Analysis for Financial Engineering
with R examples
Authors: Ruppert, David, Matteson, David S.
Free Preview- Examples using financial markets and economic data illustrate important concepts
- R Labs with real-data exercises give students practice in data analysis
- Integration of graphical and analytic methods for model selection and model checking quantify
- Helps mitigate risks due to modeling errors and uncertainty
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- About this Textbook
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The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
- About the authors
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David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering and Department of Statistical Science, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semiparametric regression, functional data analysis, biostatistics, model calibration, measurement error and astrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Journal of the American Statistical Association-Theory and Methods, former editor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics's Lecture Notes--Monographs Series and former Associate Editor of several major statistics journals. Professor Ruppert has published over 125 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.
David S. Matteson is Assistant Professor of Statistical Science, ILR School and Department of Statistical Science, Cornell University, where he is a member of the Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering courses. His research areas include multivariate time series, signal processing, financial econometrics, spatio-temporal modeling, dimension reduction, machine learning, and biostatistics. Professor Matteson received his PhD in Statistics at the University of Chicago and his BS in Finance, Mathematics, and Statistics at the University of Minnesota. He received a CAREER Award from the National Science Foundation and won Best Academic Paper Awards from the annual R/Finance conference. He is an Associate Editor of the Journal of the American Statistical Association-Theory and Methods, Biometrics, and Statistica Sinica. He is also an Officer for the Business and Economic Statistics Section of American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society.
- Table of contents (21 chapters)
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Introduction
Pages 1-4
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Returns
Pages 5-18
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Fixed Income Securities
Pages 19-43
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Exploratory Data Analysis
Pages 45-83
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Modeling Univariate Distributions
Pages 85-135
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Table of contents (21 chapters)
- Download Preface 1 PDF (30.9 KB)
- Download Sample pages 1 PDF (265.2 KB)
- Download Table of contents PDF (91.9 KB)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Statistics and Data Analysis for Financial Engineering
- Book Subtitle
- with R examples
- Authors
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- David Ruppert
- David S. Matteson
- Series Title
- Springer Texts in Statistics
- Copyright
- 2015
- Publisher
- Springer-Verlag New York
- Copyright Holder
- Springer Science+Business Media New York
- eBook ISBN
- 978-1-4939-2614-5
- DOI
- 10.1007/978-1-4939-2614-5
- Hardcover ISBN
- 978-1-4939-2613-8
- Softcover ISBN
- 978-1-4939-5173-4
- Series ISSN
- 1431-875X
- Edition Number
- 2
- Number of Pages
- XXVI, 719
- Number of Illustrations
- 113 b/w illustrations, 108 illustrations in colour
- Topics