Statistics and Data Analysis for Financial Engineering

By: David Ruppert

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Paperback | 27 December 2012

At a Glance

Paperback
662 Pages

Dimensions(cm)
23.39 x 15.6 x 3.38

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Published: 31st May 2015

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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.

Industry Reviews

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)

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