Statistics and Data Analysis for Financial Engineering

<div style=&quote;MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal&quote;>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=&quote;MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal&quote;>The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.</div><div style=&quote;MARGIN: 0in 0in 0pt; LINE-HEIGHT: normal&quote;>Some exposure to finance is helpful.</div>