The new edition of this influential textbook‎,‎ geared towards graduate or advanced undergraduate students‎,‎ teaches the statistics necessary for financial engineering‎.‎ In doing so‎,‎ it illustrates concepts using financial markets and economic data‎,‎ R Labs with real‎-data exercises‎,‎ and graphical and analytic methods for modeling and diagnosing modeling errors‎.‎ These methods are critical because financial engineers now have access to enormous quantities of data‎.‎ To make use of this data‎,‎ the powerful methods in this book for working with quantitative information‎,‎ particularly about volatility and risks‎,‎ are essential‎.‎ Strengths of this fully‎-revised edition include major additions to the R code and the advanced topics covered‎.‎ Individual chapters cover‎,‎ among other topics‎,‎ multivariate distributions‎,‎ copulas‎,‎ Bayesian computations‎,‎ risk management‎,‎ and cointegration‎.‎ Suggested prerequisites are basic knowledge of statistics and probability‎,‎ matrices and linear algebra‎,‎ and calculus‎.‎ There is an appendix on probability‎,‎ statistics and‎.‎‎.‎‎.‎