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Item specifics

Condition
Like New: A book that has been read, but looks new. The book cover has no visible wear, and the dust ...
Subject
Statistics
Subject Area
Data Analysis
ISBN
9780387202860

About this product

Product Identifiers

Publisher
Springer
ISBN-10
0387202862
ISBN-13
9780387202860
eBay Product ID (ePID)
5978799

Product Key Features

Number of Pages
Xvi, 455 Pages
Publication Name
Statistical Analysis of Financial Data in S-Plus
Language
English
Publication Year
2004
Subject
Finance / General, Applied, Statistics
Type
Textbook
Subject Area
Mathematics, Business & Economics
Author
René Carmona
Series
Springer Texts in Statistics Ser.
Format
Hardcover

Dimensions

Item Weight
86.4 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2003-066218
Dewey Edition
22
Reviews
From the reviews: As can be seen from the chapters'e(tm) contents, the breadth of topics covered of this book is impressive. Overall, this is a very nice book for introducing students to a variety of models for analyzing financial data." Journal of Statistical Software, June 2004 "The author, a fellow of the Institute of Mathematical Statistics, presents a solid dose of theory and methodology." Technometrics, May 2005 "This book is a text for an undergraduate course in data analysis focused on financial applications. It is not an S-Plus book but rather covers the main problems arising in data analysis techniques in financial engineering..As the book is based on lectures for a course on statsitical analysis of financial data, a trade off between the depth at which the toics are presented and the computational implementations are kept in balance. This textbook will be very helpful for a general course in financial engineering." The American Statistician, November 2005 "This textbook appears to be primarily intended as an introduction to statistical analysis of financial data 'e¦ . the book provides the reader with a practical computational approach to financial analytical techniques. It should appeal to instructors who prefer an applied-based text to a theoretical one. I enjoyed the use of simulation based illustrations and will be using some of the ideas in the future. The book could be used for teaching a third-year undergraduate or post-graduate (honours level), course in a statistics department or in a program designed for finance." (Gary D Sharp, SASA News, March, 2006) "S-plus, a popular software for statisticians, has many books devoted to teach it. 'e¦ the book would be very good choice as a lab manual providing many useful rules of thumb. 'e¦ the book doubtlessly provides a pleasant introduction to statistics using S-plus. The friendly tone throughout certainly adds to the charm. Simple yet detailed exercises at the end of each chapter offer a gentle massage for the brain." (Arnab Chakraborty, Sankhya, Vol. 66 (3), 2004) "This is an excellent text, written by a well known expert in the field, dealing with statistical analysis of financial data. 'e¦ As remarked by the author, the emphasis of the book is on graphical and computational methods for the analysis of financial data. 'e¦ The book is clearly written and remarkably free of typos. I believe it will be a very useful addition to the existing books and I highly recommend it." (Pedro A. Morettin, Zentralblatt MATH, Vol. 1055, 2005) "This is a timely book on modern data analysis with a difference: the examples and applications are predominantly taken from Finance Engineering. 'e¦ This book will help fill a statistical gap in the otherwise heavily theoretical literature in mathematical finance." (D. L. McLeish, Short Book Reviews, Vol. 24 (2), 2004) "The seven chapters are an excellent resource to anyone wishing to learn more about the application of statistics to financial data. 'e¦ A comprehensive reference section is given and the book has the S-PLUS codes that are needed to perform the statistical modelling. 'e¦ The reference section is extremely useful and comprehensive. Libraries should be encouraged to purchase copies of this text for undergraduate and post-graduate students in finance and statistics." (Isaac Dialsingh, Significance, Vol. 3 (3), 2006), From the reviews: As can be seen from the chapters' contents, the breadth of topics covered of this book is impressive. Overall, this is a very nice book for introducing students to a variety of models for analyzing financial data." Journal of Statistical Software, June 2004 "The author, a fellow of the Institute of Mathematical Statistics, presents a solid dose of theory and methodology." Technometrics, May 2005 "This book is a text for an undergraduate course in data analysis focused on financial applications. It is not an S-Plus book but rather covers the main problems arising in data analysis techniques in financial engineering..As the book is based on lectures for a course on statsitical analysis of financial data, a trade off between the depth at which the toics are presented and the computational implementations are kept in balance. This textbook will be very helpful for a general course in financial engineering." The American Statistician, November 2005 "This textbook appears to be primarily intended as an introduction to statistical analysis of financial data … . the book provides the reader with a practical computational approach to financial analytical techniques. It should appeal to instructors who prefer an applied-based text to a theoretical one. I enjoyed the use of simulation based illustrations and will be using some of the ideas in the future. The book could be used for teaching a third-year undergraduate or post-graduate (honours level), course in a statistics department or in a program designed for finance." (Gary D Sharp, SASA News, March, 2006) "S-plus, a popular software for statisticians, has many books devoted to teach it. … the book would be very good choice as a lab manual providing many useful rules of thumb. … the book doubtlessly provides a pleasant introduction to statistics using S-plus. The friendly tone throughout certainly adds to the charm. Simple yet detailed exercises at the end of each chapter offer a gentle massage for the brain." (Arnab Chakraborty, Sankhya, Vol. 66 (3), 2004) "This is an excellent text, written by a well known expert in the field, dealing with statistical analysis of financial data. … As remarked by the author, the emphasis of the book is on graphical and computational methods for the analysis of financial data. … The book is clearly written and remarkably free of typos. I believe it will be a very useful addition to the existing books and I highly recommend it." (Pedro A. Morettin, Zentralblatt MATH, Vol. 1055, 2005) "This is a timely book on modern data analysis with a difference: the examples and applications are predominantly taken from Finance Engineering. … This book will help fill a statistical gap in the otherwise heavily theoretical literature in mathematical finance." (D. L. McLeish, Short Book Reviews, Vol. 24 (2), 2004) "The seven chapters are an excellent resource to anyone wishing to learn more about the application of statistics to financial data. … A comprehensive reference section is given and the book has the S-PLUS codes that are needed to perform the statistical modelling. … The reference section is extremely useful and comprehensive. Libraries should be encouraged to purchase copies of this text for undergraduate and post-graduate students in finance and statistics." (Isaac Dialsingh, Significance, Vol. 3 (3), 2006), From the reviews: As can be seen from the chapters' contents, the breadth of topics covered of this book is impressive. Overall, this is a very nice book for introducing students to a variety of models for analyzing financial data." Journal of Statistical Software, June 2004 "The author, a fellow of the Institute of Mathematical Statistics, presents a solid dose of theory and methodology." Technometrics, May 2005 "This book is a text for an undergraduate course in data analysis focused on financial applications. It is not an S-Plus book but rather covers the main problems arising in data analysistechniques in financial engineering..As the book is based on lectures for a course on statsitical analysis of financial data, a trade off between the depth at which the toics are presented and the computational implementations are kept in balance. This textbook will be very helpful for a general course in financial engineering."The American Statistician, November2005 "This textbook appears to be primarily intended as an introduction to statistical analysis of financial data ... . the book provides the reader with a practical computational approach to financial analytical techniques. It should appeal to instructors who prefer an applied-based text to a theoretical one. I enjoyed the use of simulation based illustrations and will be using some of the ideas in the future. The book could be used for teaching a third-year undergraduate or post-graduate (honours level), course in a statistics department or in a program designed for finance." (Gary D Sharp, SASA News, March, 2006) "S-plus, a popular software for statisticians, has many books devoted to teach it. ... the book would be very good choice as a lab manual providing many useful rules of thumb. ... the book doubtlessly provides a pleasant introduction to statistics using S-plus. The friendly tone throughout certainly adds to the charm. Simple yet detailed exercises at the end of each chapter offer a gentle massage for the brain." (Arnab Chakraborty, Sankhya, Vol. 66 (3), 2004) "This is an excellent text, written by a well known expert in the field, dealing with statistical analysis of financial data. ... As remarked by the author, the emphasis of the book is on graphical and computational methods for the analysis of financial data. ... The book is clearly written and remarkably free of typos. I believe it will be a very useful addition to the existing books and I highly recommend it." (Pedro A. Morettin, Zentralblatt MATH, Vol. 1055, 2005) "This is a timely book on modern data analysis with a difference: the examples and applications are predominantly taken from Finance Engineering. ... This book will help fill a statistical gap in the otherwise heavily theoretical literature in mathematical finance." (D. L. McLeish, Short Book Reviews, Vol. 24 (2), 2004) "The seven chapters are an excellent resource to anyone wishing to learn more about the application of statistics to financial data. ... A comprehensive reference section is given and the book has the S-PLUS codes that are needed to perform the statistical modelling. ... The reference section is extremely useful and comprehensive. Libraries should be encouraged to purchase copies of this text for undergraduate and post-graduate students in finance and statistics." (Isaac Dialsingh, Significance, Vol. 3 (3), 2006), From the reviews: As can be seen from the chapters' contents, the breadth of topics covered of this book is impressive. Overall, this is a very nice book for introducing students to a variety of models for analyzing financial data." Journal of Statistical Software, June 2004 "The author, a fellow of the Institute of Mathematical Statistics, presents a solid dose of theory and methodology." Technometrics, May 2005 "This book is a text for an undergraduate course in data analysis focused on financial applications. It is not an S-Plus book but rather covers the main problems arising in data analysis techniques in financial engineering..As the book is based on lectures for a course on statsitical analysis of financial data, a trade off between the depth at which the toics are presented and the computational implementations are kept in balance. This textbook will be very helpful for a general course in financial engineering." The American Statistician, November 2005 "This textbook appears to be primarily intended as an introduction to statistical analysis of financial data ... . the book provides the reader with a practical computational approach to financial analytical techniques. It should appeal to instructors who prefer an applied-based text to a theoretical one. I enjoyed the use of simulation based illustrations and will be using some of the ideas in the future. The book could be used for teaching a third-year undergraduate or post-graduate (honours level), course in a statistics department or in a program designed for finance." (Gary D Sharp, SASA News, March, 2006) "S-plus, a popular software for statisticians, has many books devoted to teach it. ... the book would be very good choice as a lab manual providing many useful rules of thumb. ... the book doubtlessly provides a pleasant introduction to statistics using S-plus. The friendly tone throughout certainly adds to the charm. Simple yet detailed exercises at the end of each chapter offer a gentle massage for the brain." (Arnab Chakraborty, Sankhya, Vol. 66 (3), 2004) "This is an excellent text, written by a well known expert in the field, dealing with statistical analysis of financial data. ... As remarked by the author, the emphasis of the book is on graphical and computational methods for the analysis of financial data. ... The book is clearly written and remarkably free of typos. I believe it will be a very useful addition to the existing books and I highly recommend it." (Pedro A. Morettin, Zentralblatt MATH, Vol. 1055, 2005) "This is a timely book on modern data analysis with a difference: the examples and applications are predominantly taken from Finance Engineering. ... This book will help fill a statistical gap in the otherwise heavily theoretical literature in mathematical finance." (D. L. McLeish, Short Book Reviews, Vol. 24 (2), 2004) "The seven chapters are an excellent resource to anyone wishing to learn more about the application of statistics to financial data. ... A comprehensive reference section is given and the book has the S-PLUS codes that are needed to perform the statistical modelling. ... The reference section is extremely useful and comprehensive. Libraries should be encouraged to purchase copies of this text for undergraduate and post-graduate students in finance and statistics." (Isaac Dialsingh, Significance, Vol. 3 (3), 2006)
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
332/.01/51955
Table Of Content
ContentsPart I Data Exploration, Estimation And Simulation1 Univariate Exploratory Data Analysis 1.1 Data, Random Variables and Their Distributions 1.1.1 The PCS Data 1.1.2 The S&P 500 Index and Financial Returns 1.1.3 Random Variables and Their Distributions 1.1.4 Examples of Probability Distribution Families 1.2 First Exploratory Data Analysis Tools 1.2.1 Random Samples 1.2.2 Histograms 1.3 More Nonparametric Density Estimation 1.3.1 Kernel Density Estimation1.3.2 Comparison with the Histogram 1.3.3 S&P Daily Returns 1.3.4 Importance of the Choice of the Bandwidth 1.4 Quantiles and Q-Q Plots 1.4.1 Understanding the Meaning of Q-Q Plots 1.4.2 Value at Risk and Expected Shortfall 1.5 Estimation from Empirical Data 1.5.1 The Empirical Distribution Function 1.5.2 Order Statistics 1.5.3 Empirical Q-Q Plots 1.6 Random Generators and Monte Carlo Samples 1.7 Extremes and Heavy Tail Distributions 1.7.1 S&P Daily Returns, Once More 1.7.2 The Example of the PCS Index 1.7.3 The Example of the Weekly S&P Returns Problems Notes & Complements2 Multivariate Data Exploration 2.1 Multivariate Data and First Measure of Dependence 2.1.1 Density Estimation 2.1.2 The Correlation Coefficient 2.2 The Multivariate Normal Distribution 2.2.1 Simulation of Random Samples 2.2.2 The Bivariate Case 2.2.3 A Simulation Example 2.2.4 Let's Have Some Coffee 2.2.5 Is the Joint Distribution Normal? 2.3 Marginals and More Measures of Dependence 2.3.1 Estimation of the Coffee Log-Return Distributions 2.3.2 More Measures of Dependence2.4 Copulas and Random Simulations 2.4.1 Copulas 2.4.2 First Examples of Copula Families 2.4.3 Copulas and General Bivariate Distributions 2.4.4 Fitting Copulas 2.4.5 Monte Carlo Simulations with Copulas 2.4.6 A Risk Management Example 2.5 Principal Component Analysis 2.5.1 Identification of the Principal Components of a Data Set 2.5.2 PCA with S-Plus 2.5.3 Effective Dimension of the Space of Yield Curves 2.5.4 Swap Rate Curves Appendix 1: Calculus with Random Vectors and Matrices Appendix 2: Families of Copulas Problems Notes & ComplementsPart II Regression3 Parametric Regression 3.1 Simple Linear Regression 3.1.1 Getting the Data 3.1.2 First Plots 3.1.3 Regression Set-up 3.1.4 Simple Linear Regression 3.1.5 Cost Minimizations 3.1.6 Regression as a Minimization Problem 3.2 Regression for Prediction & Sensitivities 3.2.1 Prediction 3.2.2 Introductory Discussion of Sensitivity and Robustness 3.2.3 Comparing L2 and L1 Regressions 3.2.4 Taking Another Look at the Coffee Data 3.3 Smoothing versus Distribution Theory 3.3.1 Regression and Conditional Expectation 3.3.2 Maximum Likelihood Approach 3.4 Multiple Regression 3.4.1 Notation 3.4.2 The S-Plus Function lm 3.4.3 R2 as a Regression Diagnostic 3.5 Matrix Formulation and Linear Models 3.5.1 Linear Models 3.5.2 Least Squares (Linear) Regression Revisited 3.5.3 First Extensions 3.5.4 Testing the CAPM 3.6 Polynomial Regression 3.6.1 Polynomial Regression as a Linear Model 3.6.2 Example of S-Plus Commands 3.6.3 Important Remark 3.6.4 Prediction with Polynomial Regression 3.6.5 Choice of the Degree p 3.7 Nonlinear Regression 3.8 Term Structure of Interest Rates: A Crash Course 3.9 Parametric Yield Curve Estimation 3.9.1 Estimation Procedures 3.9.2 Practical Implementation 3.9.3 S-Plus Experiments 3.9.4 Concluding Remarks Appendix: Cautionary Notes on Some S-Plus Idiosyncracies Problems Notes & Complements4 Local & Nonparametric Regression 4.1 Review of the Regression Setup 4.2 Natural Splines as Local Smoothers 4.3 Nonparametric Scatterplot Smoothers4.3.1 Smoothing Splines 4.3.2 Locally Weighted Regression 4.3.3 A Robust Smoother 4.3.4 The Super Smoother4.3.5 The Kernel Smoother 4.4 More Yield Curve Estimation 4.4.1 A First Estimation Method 4.4.2 A Direct Application of Smoothing Splines 4.4.3 US and Japanese Instantaneous Forward Rates 4.5 Multivariate Kernel Regression 4.5.1 Running the Kernel in S-Plus 4.5.2 An Example Involving the June 1998 S&P Fu
Synopsis
Discusses analyzing financial data with S-PLUS. This title provides an introduction of the tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves.,  This book develops the use of statistical data analysis in finance, and it uses the statistical software environment of S-PLUS as a vehicle for presenting practical implementations from financial engineering. It introduces tools for the estimation and simulation of heavy tail distributions and copulas, the computation of measures of risk, and the principal component analysis of yield curves. The book is aimed at undergraduate students in financial engineering, master students in finance and MBAs, and to practitioners with financial data analysis concerns.
LC Classification Number
QA276-280HB135-147

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