Statistical Analysis of Financial Data in S-Plus - Brossura

Carmona, René

Brossura

ISBN 10: 1441919082 ISBN 13: 9781441919083

Casa editrice: Springer, 2011

Riassunto

This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. Its originality lies in the introduction of 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 MBA's, and to practitioners with financial data analysis concerns.

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Recensione

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)

Contenuti

Contents Part I Data Exploration, Estimation And Simulation 1 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 Estimation 1.3.2 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 & Complements 2 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 Dependence 2.4 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 2.5Principal 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 & Complements Part II Regression 3 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 & Complements 4 Local & Nonparametric Regression 4.1 Review of the Regression Setup 4.2 Natural Splines as Local Smoothers 4.3 Non

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2011
• ISBN 10: 1441919082
• ISBN 13: 9781441919083
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 472