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Statistics and Data Analysis for Financial Engineering

By David Ruppert

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Statistics and Data Analysis for Financial Engineering provides an overview of the methods and techniques used to extract quantitative information from enormous amounts of data. The text includes R Labs with real-data exercises, and integrates graphical and analytical  methods for modeling and diagnosing modeling errors.

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  • ISBN13: 978-1-4419-7786-1
  • 660 Pages
  • Publication Date: November 8, 2010
  • Available eBook Formats: PDF
Full Description
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.
Table of Contents

Table of Contents

  1. Introduction.
  2. Returns.
  3. Fixed income securities.
  4. Exploratory data analysis.
  5. Modeling univariate distributions.
  6. Resampling.
  7. Multivariate statistical models.
  8. Copulas.
  9. Time series models: basics.
  10. Time series models: further topics.
  11. Portfolio theory.
  12. Regression: basics.
  13. Regression: troubleshooting.
  14. Regression: advanced topics.
  15. Cointegration.
  16. The capital asset pricing model.
  17. Factor models and principal components.
  18. GARCH models.
  19. Risk management.
  20. Bayesian data analysis and MCMC.
  21. Nonparametric regression and splines.

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