Statistics and Data Analysis for Financial Engineering

✍ Scribed by David Ruppert (auth.)

• Publisher: Springer-Verlag New York
• Year: 2011
• Tongue: English
• Leaves: 662
• Series: Springer Texts in Statistics
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxii
Introduction....Pages 1-4
Returns....Pages 5-15
Fixed Income Securities....Pages 17-39
Exploratory Data Analysis....Pages 41-78
Modeling Univariate Distributions....Pages 79-130
Resampling....Pages 131-148
Multivariate Statistical Models....Pages 149-174
Copulas....Pages 175-200
Time Series Models: Basics....Pages 201-255
Time Series Models: Further Topics....Pages 257-283
Portfolio Theory....Pages 285-308
Regression: Basics....Pages 309-340
Regression: Troubleshooting....Pages 341-367
Regression: Advanced Topics....Pages 369-411
Cointegration....Pages 413-422
The Capital Asset Pricing Model....Pages 423-442
Factor Models and Principal Components....Pages 443-476
GARCH Models....Pages 477-504
Risk Management....Pages 505-529
Bayesian Data Analysis and MCMC....Pages 531-578
Nonparametric Regression and Splines....Pages 579-596
Back Matter....Pages 597-638

✦ Subjects

Statistics for Business/Economics/Mathematical Finance/Insurance