Statistics and Data Analysis for Financial Engineering
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 a
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David Ruppert
Statistics and Data Analysis for Financial Engineering
David Ruppert School of Operations Research and Information Engineering Cornell University Comstock Hall 1170 14853-3801 Ithaca New York USA [email protected] Series Editors: George Casella Department of Statistics University of Florida Gainesville, FL 32611-8545 USA
Stephen Fienberg Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213-3890 USA
Ingram Olkin Department of Statistics Stanford University Stanford, CA 94305 USA
ISSN 1431-875X ISBN 978-1-4419-7786-1 e-ISBN 978-1-4419-7787-8 DOI 10.1007/978-1-4419-7787-8 Springer New York Dordrecht Heidelberg London © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
To the memory of my grandparents
Preface
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 Chapters 4–9 and 17–20. These chapters alone are more than enough to fill a one semester course. I do not cover regression (Chapters 12–14 and 21) or the more advanced time series topics in Chapter 10, since these topics are covered in other courses. In the past, I have not covered cointegration (Chapter 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. A number of different courses might be based on this book. A two-semester sequence could cover most of the material. A one-semester course with more emphasis on finance would include Chapters 11 and 16 on portfolios and the CAPM and omit some of the chapters on statistics, for instance, Chapters 8, 18, and 20 on copulas, GARCH models, and Bayesian statistics. The book could be used for courses at both the master’s and Ph.D. levels. Readers familiar with my textbook Stati
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