Non-Linear Time Series Models in Empirical Finance | 
 enlarge | Authors: Philip Hans Franses, Dick Van Dijk
Publisher: Cambridge University Press
Category: Book
List Price: $48.00
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Rating:  7 reviews
Sales Rank: 751889
Media: Paperback
Edition: 1
Pages: 296
Number Of Items: 1
Shipping Weight (lbs): 1.4
Dimensions (in): 9.6 x 6.9 x 0.8
ISBN: 0521779650
Dewey Decimal Number: 332.015118
EAN: 9780521779654
Publication Date: September 4, 2000
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Product Description
This is the most up-to-date and accessible guide to one of the fastest growing areas in financial analysis by two of the most accomplished young econometricians in Europe. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of recently developed nonlinear models, including regime-switching and artificial neural networks, and applies them to describing and forecasting financial asset returns and volatility. It uses a wide range of financial data, drawn from sources including the markets of Tokyo, London and Frankfurt.
Book Description
The most up to-date and accessible guide to one of the fastest growing areas in financial analysis by two of the most accomplished young econometricians in Europe. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of recently developed non-linear models, including regime-switching and artificial neural networks, and applies them to describing and forecasting financial asset returns and volatility. Uses a wide range of financial data, drawn from sources including the markets of Tokyo, London and Frankfurt.
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| Customer Reviews: Read 2 more reviews...
An excellent, up-to-date guide of finance non-linear models August 22, 2001
Daniel Ventosa S (Marseille, France)
34 out of 34 found this review helpful
If you are interested in what's up nowadays in the finance modeling, you should have this book. It's a review of some of the more recent, important and promising works of the field. Advanced undergraduate students and graduate students will probably understand the book (although I recommend it mostly for people interested in the field). If you want an easy introduction of most of the topics (but pretty older), then, grab Walter Enders book or, the more complicated, but also more complete book of James D. Hamilton. Reading this manual is easy because it's clear and its style is not boring. If you really love finance econometrics, you'll find this book fun to read. The fields covered by the authors are: 1.-Linear models (pretty brief), unit roots, seasonality and aberrant observations; 2.-Regime-switching models for returns such as TAR (Threshold Autoregressive), SETAR,...; 3.-Regime switching models for volatility (and here you'll have the entire family of ARCH models, with its youngest cousins such as GARCH QGARCH, LSTGARCH, VS-GARCH); 4.-Artificial Neural Network for returns. I'm particularly interested in GARCH-type models, and I can tell this part is particularly well done. At the end of the chapter there is a very illuminating empirical comparison between the models. I cannot say if the "artificial neural networks" is a good chapter since I'm not an expert, but the least I can say is that it's pretty understandable (although quite challenging for an ignorant like myself).
nice coverage of time series methods applicable to finance February 7, 2008
Michael R. Chernick (Holland PA)
22 out of 22 found this review helpful
Like his other books, Franses provides an nice applied treatment of non-linear time series models that are in this case applicable to finance. It includes extensive coverage of regime switching models. It includes data drawn from several financial markets including Tokyo, London and Frankfurt.
nice coverage if non-linear time series April 25, 2002
Michael R. Chernick (Malvern, PA)
12 out of 15 found this review helpful
Like his other books, Franses provides an nice applied treatment of non-linear time series models that are in this case applicable to finance. It includes extensive coverage of regime switching models. It includes data drawn from several financial markets including Tokyo, London and Frankfurt.
An excellent - practical and insightful- introduction March 20, 2006
grouchy (exiled into purgatory. for real.)
3 out of 3 found this review helpful
If you are looking for a book that expands on financial econometrics beyond "The Econometrics of Financial Markets", the dated but otherwise excellent book of Campbell, Lo, and MacKinlay this is an excellent choice.
The premise is the well-known: while models used in empirical finance are linear, the nature of financial data suggests that non-linear models are more appropriate. It is particularly in forecasting and more accurately describing returns and volatility where the non-linear models offer considerable advantages over linear models.
Since there are considerable candidate non-linear time series models available for the modeler or forecaster of economic time series, selecting the right model from the get-go can be difficult. Of course, if you have had good lecture notes from your grad program, you are set. If not, then this book does help you along the way. It is an up to-date guide and provides a rigorous treatment of non-linear models. I like the regime-switching but the artificial neural networks part leaves me cold.
One of the nice things about the book is that it uses a wide range of financial data, from Tokyo, London and Frankfurt.
1. Introduction;
2. Some concepts in Time Series analysis; (Good review of TS stuff)
3. Regime-switching models for returns; (I like this part; explains everything well and easy to follow. Of course, if you are new to the area, this is hard)
4. Regime-Switching models for Volatility; (This is a tough area and they do a good job)
5. Artificial neural networks for returns;
6. Conclusion.
The GAUSS code is available at the authors' website. This is a nice feature, although I do not use GAUSS.
A Long-Awaited Update To Granger and Terasvirta's Book . January 18, 2002
Cem Payaslioglu (Famagusta, North Cyprus)
5 out of 6 found this review helpful
The major distinction of the book from Granger&Terasvirta's earlier work is its focus on financial applications of regime switching (RS) models and the author's separate treatment of RS in returns(means) and volatilities(variances) by putting them in different chapters. Another welcome feature is the availability of accompanying procedures in Gauss downloadable from the author's website. I would have expected a lengthier treatment of Markov RS models but I guess either the authors leave this to Tsay's new book or quote Hamilton as classical reference source.
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