&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& | Statistics and Data Analysis for Financial Engineering by David Ruppert, Paperback | Indigo Chapters