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© 2015

Statistics and Data Analysis for Financial Engineering

with R examples

  • 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

Textbook

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. David Ruppert, David S. Matteson
    Pages 1-4
  3. David Ruppert, David S. Matteson
    Pages 5-18
  4. David Ruppert, David S. Matteson
    Pages 19-43
  5. David Ruppert, David S. Matteson
    Pages 45-83
  6. David Ruppert, David S. Matteson
    Pages 85-135
  7. David Ruppert, David S. Matteson
    Pages 137-156
  8. David Ruppert, David S. Matteson
    Pages 157-182
  9. David Ruppert, David S. Matteson
    Pages 183-215
  10. David Ruppert, David S. Matteson
    Pages 217-248
  11. David Ruppert, David S. Matteson
    Pages 249-268
  12. David Ruppert, David S. Matteson
    Pages 269-306
  13. David Ruppert, David S. Matteson
    Pages 307-360
  14. David Ruppert, David S. Matteson
    Pages 361-404
  15. David Ruppert, David S. Matteson
    Pages 405-452
  16. David Ruppert, David S. Matteson
    Pages 453-463
  17. David Ruppert, David S. Matteson
    Pages 465-493
  18. David Ruppert, David S. Matteson
    Pages 495-515
  19. David Ruppert, David S. Matteson
    Pages 517-552
  20. David Ruppert, David S. Matteson
    Pages 553-579

About this book

Introduction

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. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, 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, multivariate volatility 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.

David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. 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 and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes—Monographs. 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 at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. 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 the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society.

Keywords

Bayesian Statistics Data Analysis for Finance Financial Analysis Financial Engineering Linear Algebra R code Statistics for Finance

Authors and affiliations

  1. 1.Department of Statistical Science & School of ORIECornell UniversityIthacaUSA
  2. 2.Department of Statistical Science Department of Social StatisticsCornell UniversityIthacaUSA

About the authors

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.

Bibliographic information

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