Statistics and Data Analysis for Financial Engineering

Front Cover
Springer Science & Business Media, Nov 8, 2010 - Business & Economics - 638 pages
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.
 

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Contents

1 Introduction
1
2 Returns
5
3 Fixed Income Securities
17
4 Exploratory Data Analysis
41
5 Modeling Univariate Distributions
79
6 Resampling
131
7 Multivariate Statistical Models
149
8 Copulas
175
Troubleshooting
341
Advanced Topics
369
15 Cointegration
413
16 The Capital Asset Pricing Model
423
17 Factor Models and Principal Components
443
18 GARCH Models
477
19 Risk Management
505
20 Bayesian Data Analysis and MCMC
531

Basics
201
Further Topics
257
11 Portfolio Theory
285
Basics
309
21 Nonparametric Regression and Splines
579
A Facts from Probability Statistics and Algebra
597
Index
623
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About the author (2010)

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' 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.

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