I have spent a number of years working through the 1st Edition Not only has the book served as a foundation for my understanding in this area, it continues to serve as a ready reference for actual projects I ve endeavored I do not consider myself a mathematical savant so working through the theory is not always easy but it is definitely doable and, I might add, necessary to have an understanding of what you re actually doing I would consider Ruppert s approach a nice balance between mathematical rigor and the pressing need for actual application A reasonable understanding of matrix operations, basic mathematical stats is definitely helpful before coming to the book but not a deal killer I have not mastered this material and some areas yet remain out of my grasp copulas, MCMC Still, I feel like a beggar who has been allowed into the banquet hall to enjoy most of the choice foods yet hasn t yet feasted on the muttonI am in much better condition than when I entered In my mind this book gave me exactly what I was looking for a launching pad for a better working knowledge of this field The R code, introduction to some key packages, and custom functions created by the author are all well placed and easy to incorporate I am getting ready to purchase the 2d edition as I understand that it has R code examples, reference to MGARCH, among other things.If you are looking to copy and paste stuff, go to StackOverflow However, if you are looking for understanding of the material and you re willing to put in a little work and not get too wound up when your understanding doesn t quite keep up with your desires, give the book a try You will shortly be doing and understanding things that you thought were out of your reach. Not enough example. Useful book, fantastic The book includes concepts that are tremendously valuable, but the author is unable to explain these concepts in a lucid manner Approximately 40% of the book is written in mathematical notation and the author rarely takes the time to define the notation that he uses At times, it seems like the author purposefully obfuscates the material because his explanations on simple financial concepts are laboriously dense The author cannot describe simple concepts such as the natural log, or normal distributions in a lucid manner In regard to difficult concepts, the reader will be spending ample time at Khan Academy and on the web attempting to deduce the notation and concepts This book is only useful for mathematicians that have a biblical grasp on mathematical notation. 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 linear algebra Practicing financial engineers will also find this book of interest