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)Statistics and Data Analysis for Financial Engineering
von David Ruppert< div style=„MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal“> 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 < em> Statistics and Finance: An Introduction</em>, 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. </div>
< div style=„MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal“> The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.</div>
< div style=„MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal“> Some exposure to finance is helpful.</div>
