Statistics and Data Analysis for Financial Engineering [Paperback]

<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 provides an overview of the methods and techniques used to extract quantitative information from enormous amounts of data. The text includes R Labs with real-data exercises, and integrates graphical and analytical? methods for modeling and diagnosing modeling errors.Introduction.- Returns.- Fixed income securities.- Exploratory data analysis.- Modeling univariate distributions.- Resampling.- Multivariate statistical models.- Copulas.- Time series models: basics.- Time series models: further topics.- Portfolio theory.- Regression: basics.- Regression: troubleshooting.- Regression: advanced topics.- Cointegration.- The capital asset pricing model.- Factor models and principal componentlÓ4