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Statistics and Data Analysis for Financial Engineering de David Ruppert

Statistics and Data Analysis for Financial Engineering: Springer Texts in Statistics

Autor David Ruppert
en Limba Engleză Paperback – 27 dec 2012
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">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 <em>Statistics and Finance: An Introduction</em>, 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. </div>
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.</div>
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">Some exposure to finance is helpful.</div>
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Specificații

ISBN-13: 9781461427490
ISBN-10: 1461427495
Pagini: 660
Ilustrații: XXII, 638 p.
Dimensiuni: 155 x 235 x 38 mm
Greutate: 0.91 kg
Ediția:2011
Editura: Springer
Colecția Springer
Seria Springer Texts in Statistics

Locul publicării:New York, NY, United States

Public țintă

Graduate

Cuprins

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.

Recenzii

From the reviews:
“Book under review is aimed at Master’s students in a financial engineering program and spans the gap between some very basic finance concepts and some very advanced statistical concepts … . The book is evidently intended as, and is best approached as, a kind of working text, giving students the opportunity to work in detail through a variety of examples. The substantial chapters on regression and time series are particularly helpful in this regard. There is lots of useful R code and many example analyses.” (R. A. Maller, Mathematical Reviews, Issue 2012 d)

Notă biografică

<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">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</div>
<div style="MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal">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 <em>Electronic Journal of Statistics</em>, former Editor of the Institute of Mathematical Statistics' <em>Lecture Notes--Monographs Series</em>, and former Associate Editor of several major statistics journals. Professor Ruppert has published over 100 scientific papers and four books: <em>Transformation and Weighting in Regression</em>, <em>Measurement Error in Nonlinear Models</em>, <em>Semiparametric Regression</em>, and <em>Statistics and Finance: An Introduction</em>.</div>

Textul de pe ultima copertă

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.

Caracteristici

Examples using financial markets and economic data illustrate important concepts R Labs with real-data exercises give students practice in data analysis Integration of graphical and analytic methods for model selection and model checking quantify and help mitigate risks due to modeling errors and uncertainty Includes supplementary material: sn.pub/extras Request lecturer material: sn.pub/lecturer-material