Statistics and Data Analysis for Financial Engineering

  • DavidĀ Ruppert

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

Table of contents

  1. Front Matter
    Pages i-xxii
  2. David Ruppert
    Pages 1-4
  3. David Ruppert
    Pages 5-15
  4. David Ruppert
    Pages 17-39
  5. David Ruppert
    Pages 41-78
  6. David Ruppert
    Pages 79-130
  7. David Ruppert
    Pages 131-148
  8. David Ruppert
    Pages 149-174
  9. David Ruppert
    Pages 175-200
  10. David Ruppert
    Pages 201-255
  11. David Ruppert
    Pages 257-283
  12. David Ruppert
    Pages 285-308
  13. David Ruppert
    Pages 309-340
  14. David Ruppert
    Pages 341-367
  15. David Ruppert
    Pages 369-411
  16. David Ruppert
    Pages 413-422
  17. David Ruppert
    Pages 423-442
  18. David Ruppert
    Pages 443-476
  19. David Ruppert
    Pages 477-504
  20. David Ruppert
    Pages 505-529
  21. David Ruppert
    Pages 531-578
  22. David Ruppert
    Pages 579-596
  23. Back Matter
    Pages 597-638

About this book


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.


Bayesian statistics portfolio management and the CAPM regression and model diagnostics risk management time series and GARCH models

Authors and affiliations

  • DavidĀ Ruppert
    • 1
  1. 1.School of Operations Research &, Information EngineeringCornell UniversityIthacaUSA

Bibliographic information