Read more
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. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, 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, 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 and an Instructor s Manual with solutions to all exercises and problems in the R labs. Practicing financial engineers will also find this book of interest.
List of contents
Introduction.- Returns.- Fixed income securities.- Exploratory data analysis.- Modeling univariate distributions.- Resampling.- Multivariate statistical models.- Copulas.- Time series models: basics.- Time series models: further topics.- Portfolio theory.- Regression: basics.- Regression: troubleshooting.- Regression: advanced topics.- Cointegration.- The capital asset pricing model.- Factor models and principal components.- GARCH models.- Risk management.- Bayesian data analysis and MCMC.- Nonparametric regression and splines.
About the author
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 several books.
Summary
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering.
