Statistics and Finance

This textbook emphasizes the applications of statistics and probability to finance.  Students are assumed to have had a prior course in statistics, but no background in finance or economics. The basics of probability and statistics are reviewed and more advanced topics in statistics, such as regression, ARMA and GARCH models, the bootstrap, and nonparametric regression using splines, are introduced as needed. The book covers the classical methods of finance such as portfolio theory, CAPM, and the Black-Scholes formula, and it introduces the somewhat newer area of behavioral finance. Applications and use of MATLAB and SAS software are stressed.

The book will serve as a text in courses aimed at advanced undergraduates and masters students in statistics, engineering, and applied mathematics as well as quantitatively oriented MBA students. Those in the finance industry wishing to know more statistics could also use it for self-study.

• Bayesian statistics
• portfolio management and the CAPM
• regression and model diagnostics
• risk management
• time series and GARCH models
• asset pricing
• bayesian statistics
• MATLAB
• probability
• Resampling
• SAS
• statistical models
• statistics
• time series
• value at risk
• quantitative finance

From the reviews:

"The inherent interaction of statistical and financial modeling makes this book a very useful and motivating instrument with which to introduce students from engineering, mathematics, statistics and economics to study statistics and/or finance." Short Book Reviews of the International Statistical Institute, December 2004

"This book will be on my list of study book sfor 2005. If you have any interest or involvement with statistics in financial applications, I recommend this book to you." Technometrics, May 2005

"...The book is well-written and clear....the clear writing with illustrative examples and pictures strongly recommend the book as a basis for finance-motivated statistics classes at the undergraduate level." SIAM Review, Vol. 47, No. 2

"David Ruppert’s … discusses computation in SAS and MATLAB. … the book is very well written and clear. … the clear writing and illustrative examples and pictures strongly recommend the book as a basis for finance-motivated statistics classes at the undergraduate level." (Ronnie Sircar, SIAM Review, Vol. 47 (2), 2005)

"That statistical methods are becoming more important in finance is further evidenced by this book from a statistician who has written some excellent … . For the statistician, this is a very good book to peruse, because it presumes no background in finance. Here the financial concepts are fully explained … . book with a considerable statistical content. … will be on my list of study books for 2005. If you have any interest in or involvement with statistics in financial applications, I recommend this book to you." (Technometrics, Vol. 47 (2), May, 2005)

"This book emphasizes the application of probability and statistics to finance by studying statistical models of financial markets … . The emphasis is on concepts rather than mathematics, and several examples are given as illustration. … . This bookshould be a valuable resource for those who are interested in the applications of probability and statistics to finance, and I believe that it will be a very useful addition to any scholarly library." (Theofanis Sapatinas, Journal of the Royal Statistical Society Series A, Vol. 168 (2), 2005)

"The inherent interaction of statistical and financial modeling makes this book a very useful and motivating instrument with which to introduce students from engineering, mathematics, statistics and economics to study statistics and/or finance. … the manuscript succeeds in covering relatively recent topics from statistics and finance, like the bootstrap, penalized splines, some VaR estimation models and behavioural finance. … Students having gained confidence with the material of this book can also be expected to be ready for advanced topics … ." (F. Trojani, Short Book Reviews International Statistical Institute, Vol. 24 (3), 2004)

"...Ruppert's book succeeds at presenting this classic material in a concises, readable way that is suitable for a wide audience including undergraduate business, economics, and statistics majors, MBA students, and master's level engineering students." Journal of the American Statistical Association, June 2006

"The book under review is about statistical concepts as applied to financial theory. … The book provides a coherent introduction of how to handle financial data by means of statistics. … Due to a very nontechnical language and a lot of examples using SAS or MATLAB the book is suitable for undergraduates and master students in statistics, applied mathematics and finance. But it can also be used for self-study." (Karsten Webel, Statistical Papers, Vol. 47, 2006)

"As the title indicates, this is a textbook on Statistics and its application in Finance. … The book is well written … . It is … suited as a text for an introduction to Statistics and Finance in a more applied department, e.g.Industrial Engineering or Operations Research. … it is a well written book, that nicely illustrates the interplay between Statistics and Finance. It is also useful as a source for additional teaching material." (T. de Wet, SASA News, June, 2005)

"The material of the book is organized in 14 chapters. … Students will love the conclusive summaries best … . David Ruppert knows how to hold the interest of his readers. Often little anecdotes on the discovery of new results are included and the data are chosen such that delightful features can be detected. So instructors can also use the book in a pure statistics course … . The topic is hot since ‘everyone is interested in money’." (Norman Fickel, Allgemeines Statistisches Archiv, Vol. 89, 2005)

"This is a very lucid textbook emphasizing the application of statistics and probability to finance. The material is always backed up by realistic examples from finance. … Indeed following these may serve as a quick and easy way of learning the basics of these softwares. Short bibliographic notes at the end of each chapter are extremely useful." (Arup Bose, Sankhya, Vol. 67 (1), 2005)

"The book ‘Statistics and Finance’ by David Ruppert discusses many financial models. … This book is appropriate for the third and fourth year undergraduate and master level courses. It will be useful to the practicing financial engineer. It assumes some background in probability and statistics. The book is interesting from both the statistical modeling and the finance perspectives." (Qin Lu, Zentralblatt MATH, Vol. 1049, 2004)

"The text book emphazises the applications of statistics and probability to finance. … The book will serve as a text in courses aimed at advanced undergraduates and masters students in statistics, engineering, and applied mathematics as well as quantitatively oriented MBA students. Those in the finance industry wishing to know more statistics could also use it for self-study."(Zentralblatt für Didaktik der Mathematik, July, 2004)

• School of Operations Research and Industrial Engineering, Cornell University, Ithaca, USA

  David Ruppert

David Ruppert is the Andrew Schultz, Jr. Professor of Engineering, School of Operations Research and Industrial Engineering, Cornell University.  He received a PhD in Statistics from Michigan State University in 1977 and taught for ten years in the Department of Statistics at the University of North Carolina at Chapel Hill.  He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and a winner of the Wilcoxon Prize for the best practical applications paper in Technometrics.  He is former Editor of the Institute of Mathematical Statistics's Lecture Notes-Monographs Series, former Associate Editor of The American Statistician and The Annals of Statistics, and currently Associate Editor of Biometrics and The Journal of the American Statistical Associate. He has published over 80 scientific papers and three books, Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, and Semiparametric Regression.

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