Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics)

Ruppert, David; Matteson, David S.

ISBN 10: 1493926136 / ISBN 13: 9781493926138

Verlag: Springer, 2015

Beschreibung

Signs of wear and consistent use. Bestandsnummer des Verkäufers 3IIT03005BZT_ns

Bibliografische Details

Titel: Statistics and Data Analysis for Financial ...

Verlag: Springer

Erscheinungsdatum: 2015

Einband: Hardcover

Zustand: Good

Auflage: 2. Auflage

Über diesen Titel

Inhaltsangabe:

<P>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. PRACTICING FINANCIAL ENGINEERS WILL ALSO FIND THIS BOOK OF INTEREST. </P>

Reseña del editor:

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. Practicing financial engineers will also find this book of interest.