9781493926138-1493926136-Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics)

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

ISBN-13: 9781493926138
ISBN-10: 1493926136
Edition: 2nd ed. 2015
Author: David Ruppert, David S. Matteson
Publication date: 2015
Publisher: Springer
Format: Hardcover 745 pages
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Book details

ISBN-13: 9781493926138
ISBN-10: 1493926136
Edition: 2nd ed. 2015
Author: David Ruppert, David S. Matteson
Publication date: 2015
Publisher: Springer
Format: Hardcover 745 pages

Summary

Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics) (ISBN-13: 9781493926138 and ISBN-10: 1493926136), written by authors David Ruppert, David S. Matteson, was published by Springer in 2015. With an overall rating of 4.4 stars, it's a notable title among other Econometrics & Statistics (Economics, Finance, Statistics, Education & Reference) books. You can easily purchase or rent Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics) (Hardcover) from BooksRun, along with many other new and used Econometrics & Statistics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $12.39.

Description

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.

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