Non-Linear Time Series Models in Empirical Finance

Rating:
Summary: An excellent, up-to-date guide of finance non-linear models
Review: 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).

Rating:
Summary: nice coverage if non-linear time series
Review: 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.

Rating:
Summary: nice coverage if non-linear time series
Review: 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.

Rating:
Summary: A Long-Awaited Update To Granger and Terasvirta's Book .
Review: 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.

Rating:
Summary: A Long Awaited Update To Granger and Temasvirta's Book
Review: 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 strategy of separate treatment of RS of returns(means) and volatilities(variances) by putting them in different chapters. Another wellcome 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.

Rating:
Summary: A Long-Awaited Update To Granger and Terasvirta's Book .
Review: 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.

Rating:
Summary: A Long Awaited Update To Granger and Temasvirta's Book
Review: 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 strategy of separate treatment of RS of returns(means) and volatilities(variances) by putting them in different chapters. Another wellcome 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.

Rating:
Summary: A timely survey on an important area
Review: The title of this book caught my attention immediately and it actually contains more interesting topics than I thought. After I bought a copy through Amazon and have a closer read, I'm not disapointed by the two authors' writing, which is probably partially based on the second author's PhD dissertation, and so it is a little narrow-focused. But as the authors stated, they want to produce a book which deals with nonlinear techniques as opposed to Mills's mostly linear methods in fiance time series. They have delivered. With hot topics such as regime switching, ARCH models, and neural network applications in finance, I'm sure this book will find a lot of interested readers and will be a key reference in nonlinear empirical finance.

Rating:
Summary: A timely survey on an important area
Review: The title of this book caught my attention immediately and it actually contains more interesting topics than I thought. After I bought a copy through Amazon and have a closer read, I'm not disapointed by the two authors' writing, which is probably partially based on the second author's PhD dissertation, and so it is a little narrow-focused. But as the authors stated, they want to produce a book which deals with nonlinear techniques as opposed to Mills's mostly linear methods in fiance time series. They have delivered. With hot topics such as regime switching, ARCH models, and neural network applications in finance, I'm sure this book will find a lot of interested readers and will be a key reference in nonlinear empirical finance.