作者：DavidRuppert
出版社：Springer
出版年：2010-11-17
页数：660
定价：USD99.00
装帧：Hardcover
ISBN：9781441977861
内容简介
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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 and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.
作者简介
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David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semiparametric regression, functional data analysis, biostatistics, model calibration, measurement error, and astrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics's Lecture Notes–Monographs Series, and former Associate Editor of several major statistics journals. Professor Ruppert has published over 100 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.
目录
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1. Introduction.
2. Returns.
3. Fixed income securities.
4. Exploratory data analysis.
5. Modeling univariate distributions.
6. Resampling.
7. Multivariate statistical models.
8. Copulas.
9. Time series models: basics.
10. Time series models: further topics.
11. Portfolio theory.
12. Regression: basics.
13. Regression: troubleshooting.
14. Regression: advanced topics.
15. Cointegration.
16. The capital asset pricing model.
17. Factor models and principal components.
18. GARCH models.
19. Risk management.
20. Bayesian data analysis and MCMC.
21. Nonparametric regression and splines.
评论 ······
1、非常非常棒的一本书，几乎每章内容都是精品。尤其是回归和时序这两部分
2、第4、5、6、7、8章可以给你打下良好的统计基础
这本书非常详细，实体书拿在手里跟字典一样而且还是大开本印刷。最适合这本书的人并不是需要从头学起的人，而是那些有一定积淀现在需要跨界的人。书中涉及的专题非常之多，而且就我看过的几章来说，严谨但也仅仅能够作为导论而已。所以最适合的读者是希望快速了解一个细分方向的人。
textbook
typos。。。in general a good book. like the R lab section.
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