Statistics and Data Analysis for Financial Engineering

with R examples

Business & Finance, Economics, Statistics, Nonfiction, Science & Nature, Mathematics, Applied
Cover of the book Statistics and Data Analysis for Financial Engineering by David Ruppert, David S. Matteson, Springer New York
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Author: David Ruppert, David S. Matteson ISBN: 9781493926145
Publisher: Springer New York Publication: April 21, 2015
Imprint: Springer Language: English
Author: David Ruppert, David S. Matteson
ISBN: 9781493926145
Publisher: Springer New York
Publication: April 21, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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