Springer Texts in Statistics

Statistics and Data Analysis for Financial Engineering

Authors: Ruppert, David

  • 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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eBook $74.99
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  • ISBN 978-1-4419-7787-8
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  • Immediate eBook download after purchase
Softcover $99.00
price for USA
  • ISBN 978-1-4614-2749-0
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About this Textbook

Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.

David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, 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 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 100 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.

About the authors

<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in</div>
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">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 <em>Electronic Journal of Statistics</em>, former Editor of the Institute of Mathematical Statistics' <em>Lecture Notes--Monographs Series</em>, and former Associate Editor of several major statistics journals. Professor Ruppert has published over 100 scientific papers and four books: <em>Transformation and Weighting in Regression</em>, <em>Measurement Error in Nonlinear Models</em>, <em>Semiparametric Regression</em>, and <em>Statistics and Finance: An Introduction</em>.</div> 

Reviews

From the reviews:

“Book under review is aimed at Master’s students in a financial engineering program and spans the gap between some very basic finance concepts and some very advanced statistical concepts … . The book is evidently intended as, and is best approached as, a kind of working text, giving students the opportunity to work in detail through a variety of examples. The substantial chapters on regression and time series are particularly helpful in this regard. There is lots of useful R code and many example analyses.” (R. A. Maller, Mathematical Reviews, Issue 2012 d)

Table of contents (21 chapters)

Buy this book

eBook $74.99
price for USA (gross)
  • ISBN 978-1-4419-7787-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $99.00
price for USA
  • ISBN 978-1-4614-2749-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Statistics and Data Analysis for Financial Engineering
Authors
Series Title
Springer Texts in Statistics
Copyright
2011
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media, LLC
eBook ISBN
978-1-4419-7787-8
DOI
10.1007/978-1-4419-7787-8
Softcover ISBN
978-1-4614-2749-0
Series ISSN
1431-875X
Edition Number
1
Number of Pages
XXII, 638
Topics