实例介绍
【实例简介】
Publication Date: November 17, 2010 | ISBN-10: 1441977864 | ISBN-13: 978-1441977861 | Edition: 2011 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
David ruppert Statistics and Data Analysis for Financial Engineering ②)s ringer David ruppert School of operations research and Information Engineering Cornell university Comstock hal 1170 14853-3801 Ithaca New York USA dr24@cornell.edu series editors George casella Stephen Fienberg Ingram Olkin Department of Statistics Department of Statistics Department of Statistics University of florida Carnegie Mellon University Stanford University Gainesville, FL 32611-8545 Pittsburgh, PA 15213-3890 Stanford, CA 94305 USA USA USA ISSN1431-875X ISBN978-1-4419-7786-1 e-ISBN978-1-4419-7787-8 DOI10.1007978-1-4419-7787-8 Springer New York Dordrecht Heidelberg London C 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 SpringerispartofSpringerScience+businessMedia(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 Cor- nell University. These students have already taken courses in portfolio man- agement, fixed income securities, options, and stochastic calculus, so I con centrate on teaching statistics, data analysis, and the use of R, and I cover most sections of Chapters 49 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 cointegra- tion(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 Statistics and Finance: An Introduc tion may wonder how that volume differs from this book. This book is at a somewhat more advanced level and has much broader coverage of topics in statistics compared to the earlier book. As the title of this volume suggests there is more emphasis on data analysis and this book is intended to be more than just "an introduction. " Chapters 8, 15, and 20 on copulas, cointegration and Bayesian statistics are new. Except for some figures borrowed from Statis- tics and Finance, in this book R is used exclusively for computations, data analysis, and graphing, whereas the earlier book used SAS and MATLAB Nearly all of the examples in this book use data sets that are available in R so readers can reproduce the results. In Chapter 20 on Bayesian statistics WinBugs is used for Markov chain Monte Carlo and is called from R using P reface the R2WinBUGS package. There is some overlap between the two books, and in particular, a substantial amount of the material in Chapters 2, 3, 9, 11-13 and 16 has been taken from the earlier book. Unlike Statistics and finance this volume does not cover options pricing and behavioral finance The prerequisites for reading this book are knowledge of calculus, vectors and matrices; probability including stochastic processes; and statistics typical of third- or fourth-year undergraduates in engineering, mathematics, statis tics, and related disciplines. There is an appendix that reviews probability and statistics, but it is intended for reference and is certainly not an introduction for readers with little or no prior exposure to these topics. Also, the reader should have some knowledge of computer programming. Some familiarity with the basic ideas of finance is helpful 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 website and R's online help) to learn more about the functions used in the abs. 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 ter 11 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 website 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 experi- ence 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 Ithaca. New York David Ruppert July 2010 Contents Notation ,,,,XⅩ1 1 Introduction 1.1 Bibliographic Notes 1.2 References 4 2 Returns 2.1 Introduction 2.1.1 Net Returns 2.1.2 Gross returns 2. 1.3 Log Returns 2. 1.4 Adjustment for Dividends 667 2.2 The Random Walk model 2.2.1 Random Walks 2.2.2 Geometric Random Walks 2.2.3 Are Log Prices a Lognormal Geometric Random Walk? 9 2.3 Bibliographic Notes ...10 2.4 References 10 2.5 R Lab 11 2.5.1 Data Analysis 11 2.5.2 Simulations 12 2.6 Exercises 14 3 Fixed Income securities 3.1 Introduction 17 3.2 Zero-Coupon bonds 18 3.2.1 Price and returns fluctuate with the interest rate... 18 3.3 Coupon Bonds 3.3.1 A General formula 20 3.4 Yield to maturit 3.4. 1 General Method for Yield to maturity 22 【实例截图】
【核心代码】
Publication Date: November 17, 2010 | ISBN-10: 1441977864 | ISBN-13: 978-1441977861 | Edition: 2011 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
David ruppert Statistics and Data Analysis for Financial Engineering ②)s ringer David ruppert School of operations research and Information Engineering Cornell university Comstock hal 1170 14853-3801 Ithaca New York USA dr24@cornell.edu series editors George casella Stephen Fienberg Ingram Olkin Department of Statistics Department of Statistics Department of Statistics University of florida Carnegie Mellon University Stanford University Gainesville, FL 32611-8545 Pittsburgh, PA 15213-3890 Stanford, CA 94305 USA USA USA ISSN1431-875X ISBN978-1-4419-7786-1 e-ISBN978-1-4419-7787-8 DOI10.1007978-1-4419-7787-8 Springer New York Dordrecht Heidelberg London C 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 SpringerispartofSpringerScience+businessMedia(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 Cor- nell University. These students have already taken courses in portfolio man- agement, fixed income securities, options, and stochastic calculus, so I con centrate on teaching statistics, data analysis, and the use of R, and I cover most sections of Chapters 49 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 cointegra- tion(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 Statistics and Finance: An Introduc tion may wonder how that volume differs from this book. This book is at a somewhat more advanced level and has much broader coverage of topics in statistics compared to the earlier book. As the title of this volume suggests there is more emphasis on data analysis and this book is intended to be more than just "an introduction. " Chapters 8, 15, and 20 on copulas, cointegration and Bayesian statistics are new. Except for some figures borrowed from Statis- tics and Finance, in this book R is used exclusively for computations, data analysis, and graphing, whereas the earlier book used SAS and MATLAB Nearly all of the examples in this book use data sets that are available in R so readers can reproduce the results. In Chapter 20 on Bayesian statistics WinBugs is used for Markov chain Monte Carlo and is called from R using P reface the R2WinBUGS package. There is some overlap between the two books, and in particular, a substantial amount of the material in Chapters 2, 3, 9, 11-13 and 16 has been taken from the earlier book. Unlike Statistics and finance this volume does not cover options pricing and behavioral finance The prerequisites for reading this book are knowledge of calculus, vectors and matrices; probability including stochastic processes; and statistics typical of third- or fourth-year undergraduates in engineering, mathematics, statis tics, and related disciplines. There is an appendix that reviews probability and statistics, but it is intended for reference and is certainly not an introduction for readers with little or no prior exposure to these topics. Also, the reader should have some knowledge of computer programming. Some familiarity with the basic ideas of finance is helpful 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 website and R's online help) to learn more about the functions used in the abs. 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 ter 11 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 website 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 experi- ence 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 Ithaca. New York David Ruppert July 2010 Contents Notation ,,,,XⅩ1 1 Introduction 1.1 Bibliographic Notes 1.2 References 4 2 Returns 2.1 Introduction 2.1.1 Net Returns 2.1.2 Gross returns 2. 1.3 Log Returns 2. 1.4 Adjustment for Dividends 667 2.2 The Random Walk model 2.2.1 Random Walks 2.2.2 Geometric Random Walks 2.2.3 Are Log Prices a Lognormal Geometric Random Walk? 9 2.3 Bibliographic Notes ...10 2.4 References 10 2.5 R Lab 11 2.5.1 Data Analysis 11 2.5.2 Simulations 12 2.6 Exercises 14 3 Fixed Income securities 3.1 Introduction 17 3.2 Zero-Coupon bonds 18 3.2.1 Price and returns fluctuate with the interest rate... 18 3.3 Coupon Bonds 3.3.1 A General formula 20 3.4 Yield to maturit 3.4. 1 General Method for Yield to maturity 22 【实例截图】
【核心代码】
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