I developed this textbook while teaching the course Statistics for Financial Engineering to master’s students in the financial engineering program at Cornell University. These students have already taken courses in portfolio management, fixed income securities, options, and stochastic calculus, so I concentrate on teaching statistics, data analysis, and the use of R, and I cover most sections of Chaps. 4–12 and 18–20. These chapters alone are more than enough to fill a one-semester course. I do not cover regression (Chaps. 9–11 and 21) or the more advanced time series topics in Chap. 13, since these topics are covered in other courses. In the past, I have not covered cointegration (Chap. 15), but I will in the future. The master’s students spend much of the third semester working on projects with investment banks or hedge funds. As a faculty adviser for several projects, I have seen the importance of cointegration.

This book does not teach R programming, but each chapter has an “R lab” with data analysis and simulations. Students can learn R from these labs and by using R’s help or the manual An Introduction to R (available at the CRAN web site and R’s online help) to learn more about the functions used in the labs. Also, the text does indicate which R functions are used in the examples. Occasionally, R code is given to illustrate some process, for example, in Chap. 16 finding the tangency portfolio by quadratic programming. For readers wishing to use R, the bibliographical notes at the end of each chapter mention books that cover R programming and the book’s web site contains examples of the R and WinBUGS code used to produce this book. Students enter my course Statistics for Financial Engineering with quite disparate knowledge of R. Some are very accomplished R programmers, while others have no experience with R, although all have experience with some programming language. Students with no previous experience with R generally need assistance from the instructor to get started on the R labs. Readers using this book for self-study should learn R first before attempting the R labs.