Statistics and Data Analysis for Financial...

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Beschreibung

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.

The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.

Some exposure to finance is helpful.

Über den Autor

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.

Zusammenfassung

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: [...]

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Inhaltsverzeichnis

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.

Details

Erscheinungsjahr:	2012
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Importe , Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xxii 638 S.
ISBN-13:	9781461427490
ISBN-10:	1461427495
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Ruppert, David
Hersteller:	Springer US Springer New York Springer US, New York, N.Y.
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	235 x 155 x 36 mm
Von/Mit:	David Ruppert
Erscheinungsdatum:	27.12.2012
Gewicht:	0,984 kg

Artikel-ID: 106094546