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eBook Modeling Financial Time Series with S-PLUS download

by Jiahui Wang,Eric Zivot

eBook Modeling Financial Time Series with S-PLUS download ISBN: 0387955496
Author: Jiahui Wang,Eric Zivot
Publisher: Springer (September 12, 2003)
Language: English
Pages: 632
ePub: 1444 kb
Fb2: 1915 kb
Rating: 4.1
Other formats: lit azw doc rtf
Category: Math Sciences
Subcategory: Mathematics

by Eric Zivot (Author), Jiahui Wang (Author). It provides theoretical and empirical discussions on exhaustive topics in modern financial econometrics, statistics and time series.

by Eric Zivot (Author), Jiahui Wang (Author). Thomas L. Burr for Techommetrics, Vol. 49, No. 1, February 2007). This book has a double function.

Authors: Zivot, Eric, Wang, Jiahui. Journal of the American Statistical Association, June 2004. With Modeling Financial Time Series with S-PLUS, Zivot and Wang deliver an impressive tour de force covering many relevant topics in modern financial econometrics. price for USA in USD (gross). As the table of contents outlines, the bookincludes anything from modern time series methods to recent advances in risk management, multivariate data analysis as applied to portfolio management, yiled-curve modeling to two detailed chapters on the already classic unvariate and multivariate GARCH-type volatitlity models.

Eric Zivot, Jiahui Wang. in Economics from the University of Washington in 1997. This is the first book to show the power of S-PLUS for the analysis of time series data. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts.

Time Series Regression Modeling. 167. Univariate GARCH Modeling. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching.

More effective time-series analysis and forecasting. November 1995 · Journal of Computational and Applied Mathematics. For this objective, we assume no specialist knowledge, as we start by surveying all those standard ideas of univariate analysis which are needed for the subsequent.

Jiahui Wang, Eric Zivot. Place of Publication.

Описание: Gives an introduction to financial econometric models and their applications to modeling and prediction of financial time series data.

Modeling Financial Time Series with S-PLUS Zivot Eric, Wang Jiahui Springer 9780387955490 : The book is unique in that it will serve as a users manual for the S- Plus module S+FinMetrics and . Описание: Gives an introduction to financial econometric models and their applications to modeling and prediction of financial time series data.

Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS.

Full recovery of all data can take up to 2 weeks! So we came to the decision at this time to double the download limits for all users until the problem is completely resolved. Thanks for your understanding! Progress: 9. % restored. Главная Modeling Financial Time Series with S-Plus®. Modeling Financial Time Series with S-Plus®. Eric Zivot, Jiahui Wang (auth.

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of the Journal of Business and Economic Statistics and Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is a Research Scientist at Insightful Corporation. He received a Ph.D. in Economics from the university of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.
Comments: (6)
Nalmezar
Zivot and Wang have done a phenomenal job of covering intermediate to advanced topics in econometrics along with the S programming language. Extensive literature reviews are coupled with robust examples and mathematics, and topped off with S code. I am a quantitative hedge fund manager, and I use the Open Source R package [..] and RMetrics [..]. I can adapt every single excercise in "Modeling Financial Time Series with S-PLUS" to use in R, and make use of them in my work. If I have one complaint it is that the book does not cover non-linear models like quantile regression or least squares, or optimization for much more than trivial two or three asset portfolios.
Thordira
I'd like to do some comparative analysis here: Matlab's GARCH Toolbox has GARCH, GJR(TGARCH), EGARCH specifications for the volatility term. A single (out of many more) procedure "garch" in S+Finmetrics has that plus PGARCH and three GARCH-M options. Given how expensive Matlab GARCH Toolbox is, none could hope to get a more advanced S+Finmetrics pack for $57. I guess the people who expected otherwise knew nothing about SPLUS and wrongly assumed that "base" SPLUS is the econometric package in question.

Apart from that, I side with Yin Luo: this book is a good mixture of basic theory and fairly complicated, real-life examples. Actually, it can even be used as a tool to refresh one's theoretical "model specification database" because it covers a wide range of many families of models in a single book. However, being mainly a S+FinMetrics manual, it doesn't go so far as to teach you the model selection. For that, a good addition would be Analysis of Financial Time Series (Wiley Series in Probability and Statistics).
Coiril
Just to be clear: buying this book does not mean you are buying S+Finmetrics. You need to purchase Splus base + the Finmetrics module separately. Unfortunately I tried to call SPLUS (twice) to obtain an academic license, and no one ever called me back. I ended up getting a copy from my university.

I wish SPLUS would set up an online download, where I can simply pay with a credit card and download the product immediately, instead of dealing with sales people. That's a very archaic distribution system in my opinion.

But this review is about this book. In fact, this book is AMAZING. It is basically a unique combination of a S+Finmetrics userguide and a primer on financial econometrics. It covers virtually all aspects of modern financial econometrics with an emphasis on practical examples. Theory is discussed to illustrate and motivate the examples. There are no proofs. If you want understand, say, a Vector Autoregression foreasting error decomposition, are you going to slog through Hamilton's "Time Series Analysis" and try to implement it on your own? No, you are going to turn to the nice tidy description in Ch11 of this book, and then call the "fevd" method, so you know what is doing and how to interpret the results.

A note on R vs. S+Finmetrics: much of the functionality in S+ Finmetrics is available in R, it's just spread across a lot of different packages. The advantage of a commercial product such as S+ Finmetrics is that it consolidates these packages, and provides (more or less) standardized methods and classes to support them.

For example, in R it is possible to fit a long memory ARIMA model using the function fracdiff. However in R the function fracdiff does not return residuals, the inclusion of exogenous x variables or support forecasting (no predict method). In SPLUS, the same function (FARIMA) returns all of these.
Oveley
This is an excellent book on financial econometrics, very practical yet rigorous. I wish all econometrics/statistics textbook could like this. Basic theory followed by practical examples - real life examples, not simplified ones like in other books. The authors gave detailed instructions on how to implement various econometric models, i.e. multi-factor models, GARCH, MGARCH, long memory models, state-space, etc. Most econometrics textbooks are at two extremes, they are either too theoretical (you still don't know how to put those models in real life), or too simple (lack of mathematical rigor and without advanced applications). This book is a combination of both worlds, computer codes/math models, and real life examples (some really good ones). A lot of cutting-edge techniques and advanced topics are also covered.
Dolid
The best thing about this book is that it combines financial time series analysis with "real-life" examples that are either reproducible or easily adaptable. Being that it is also the user manual for the S+FinMetrics module for the SPLUS stats. package it also reads like a software manual (some people like that). This book provides a good sample of many time series techniques that can be applied out of the box.
Note: The S+FinMetrics module includes this book.
Hiylchis
As other reviewers have mentioned, this book is useless without FinMetrics. It is merely a user manual for that package, and has hardly any intrinsic value on its own.