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An Introduction to Wavelets and Other Filtering Methods in Finance and Economics [Hardcover]

Ramazan Gençay , Faruk Selçuk , Brandon J. Whitcher
5.0 out of 5 stars  See all reviews (2 customer reviews)
Price: CDN$ 136.00 & FREE Shipping. Details
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Book Description

Sept. 12 2001 0122796705 978-0122796708 1
An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics. It emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general) can reveal about a time series. It offers testing issues which can be performed with wavelets in conjunction with the multi-resolution analysis. The descriptive focus of the book avoids proofs and provides easy access to a wide spectrum of parametric and nonparametric filtering methods. Examples and empirical applications will show readers the capabilities, advantages, and disadvantages of each method.

*The first book to present a unified view of filtering techniques

*Concentrates on exactly what wavelets analysis and filtering methods in general can reveal about a time series

*Provides easy access to a wide spectrum of parametric and non-parametric filtering methods

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Review

"There are many books on linear filters and wavelets, but there is only one book, Gençay, Selçuk, and Whitcher, that provides an introduction to the field for economists and financial analysts and the motivation to study the subject.....[it] contains many practical economic and financial examples that will stimulate academic and professional research for years to come...a most welcome addition to the wavelet literature."
James B. Ramsey, Professor of Economics, New York University, USA

"...particularly recommended for any time series econometrician wanting to keep up to date".
Clive W. Granger, Professor of Economics, University of California, San Diego, USA

"This timely volume will be of interest to anyone who wants to understand the latest technology for analyzing economic and financial time series. The authors are to be commended for their clear and comprehensive presentation of a fascinating and powerful approach to time-series analysis".
Halbert White, University of California, San Diego, USA

From the Back Cover

"The authors present, in a simple fashion, a new class of filters that greatly expands on those previously available, allowing greater flexibility and generating models with time-varying specifications. The book considers familiar techniques and shows how these can be viewed in new ways, illustrating them with empirical studies from finance. It is particularly recommended for any time series econometrician wanting to keep up to date."
--Clive W. J. Granger, Professor of Economics, University of California, San Diego
"There are many books on linear filters and wavelets, but there is only one book, Gençay, Selçuk, and Whitcher, that provides an introduction to the field for economists and financial analysts and the motivation to study the subject. This book contains many practical economic and financial examples that will stimulate academic and professional research for years to come. This book is a most welcome addition to the wavelet literature."
--James B. Ramsey, Professor of Economics, New York University
"The authors have provided a very comprehensive account of the filtering literature, including wavelets, a tool not widely used in economics and finance. The volume includes many numerical illustrations, and should be accessible to a wide range of researchers."
--Peter M. Robinson, Tooke Professor of Economic Science and Statistics and Leverhulme Research Professor, London School of Economics, U.K.
"This timely volume will be of interest to anyone who wants to understand the latest technology for analyzing economic and financial time series. The authors are to be commended for their clear and comprehensive presentation of a fascinating and powerful approach to time-series analysis."
--Halbert White, University of California, San Diego
What can wavelet analysis tell us about time series? Filled with empirical applications from economics and finance, this book presents a unified view of filtering techniques. It provides easy access to a wide spectrum of parametric and nonparametric filtering methods, moving from older, well-known methods to newer ones. Avoiding proofs as much as possible and emphasizing explanations and underlying theories, the authors ensure that both those who are familiar with wavelets and those who ought to be have a definitive book that reveals the capabilities, advantages, and disadvantages of each method.

Inside This Book (Learn More)
First Sentence
The fundamental reason of writing this book is that we believe the basic premise of wavelet filtering provides insight into the dynamics of economic/financial time series beyond that of current methodology. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index
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Most helpful customer reviews
5.0 out of 5 stars The Guide Dec 24 2001
By A Customer
Format:Hardcover
Various types of non-stationarities are common in time series data from financial markets. This requires a guide for selecting among numerous tools to deal with the non-stationarity. A unified treatment of filters like this book is a great help since it provides a fast and rigorous introduction.

Chapter 2 is on the general linear filtering theory with cleverly designed applications for illustrative purposes. "Optimum Linear Estimation" is the focus of Chapter 3 in which the Wiener Filter and the Kalman Filters among others are studied. Chapter 4 is on Discrete Wavelet Transforms and provides applications like filtering intraday seasonality in FX market and an examination of the relation between money growth and inflation. Long memory processes with seasonal components are analyzed using wavelets in Chapter 5. Denoising of economics and financial time series is the topic of Chapter 6. The decomposition of variance across different frequency bands as well as the cross-covariance between two time-series at different scales is covered in Chapter 7. Finally, Chapter 8 is on artificial neural networks in which both an introduction to the concept and some design issues with appropriate model selection criteria are provided.
Discussison of these relatively advanced topics is very simple and clear without sacrificing important details. Highly recommended.
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5.0 out of 5 stars Easy to understand! Oct. 27 2001
By A Customer
Format:Hardcover
The book is a wonderful reference in that it brings together various filtering methods. It is an excellent introduction to the topic, clearly written and easy to understand. The text does not assume a high-level math background. Further, unlike the various books which simply provide the theory but include very few or no applications at all, this book by Gencay, Selcuk, and Whitcher has many applications that help you get the right picture.
Was this review helpful to you?
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com: 4.7 out of 5 stars  3 reviews
12 of 12 people found the following review helpful
5.0 out of 5 stars The Guide Dec 24 2001
By A Customer - Published on Amazon.com
Format:Hardcover
Various types of non-stationarities are common in time series data from financial markets. This requires a guide for selecting among numerous tools to deal with the non-stationarity. A unified treatment of filters like this book is a great help since it provides a fast and rigorous introduction.

Chapter 2 is on the general linear filtering theory with cleverly designed applications for illustrative purposes. "Optimum Linear Estimation" is the focus of Chapter 3 in which the Wiener Filter and the Kalman Filters among others are studied. Chapter 4 is on Discrete Wavelet Transforms and provides applications like filtering intraday seasonality in FX market and an examination of the relation between money growth and inflation. Long memory processes with seasonal components are analyzed using wavelets in Chapter 5. Denoising of economics and financial time series is the topic of Chapter 6. The decomposition of variance across different frequency bands as well as the cross-covariance between two time-series at different scales is covered in Chapter 7. Finally, Chapter 8 is on artificial neural networks in which both an introduction to the concept and some design issues with appropriate model selection criteria are provided.
Discussison of these relatively advanced topics is very simple and clear without sacrificing important details. Highly recommended.
10 of 10 people found the following review helpful
5.0 out of 5 stars Easy to understand! Oct. 26 2001
By A Customer - Published on Amazon.com
Format:Hardcover
The book is a wonderful reference in that it brings together various filtering methods. It is an excellent introduction to the topic, clearly written and easy to understand. The text does not assume a high-level math background. Further, unlike the various books which simply provide the theory but include very few or no applications at all, this book by Gencay, Selcuk, and Whitcher has many applications that help you get the right picture.
1 of 1 people found the following review helpful
4.0 out of 5 stars More of an introduction to techniques in non-parametric regression Aug. 3 2010
By Scott C. Locklin - Published on Amazon.com
Format:Hardcover|Verified Purchase
Despite the title, I don't find this book particularly useful as a book on wavelets. For one thing, a good fraction of the book is about filters or non-parametric regression in general; probably more than half. Some of the wavelet techniques are useful in finance; I wish more of these techniques were encoded in their R waveslim package. I strongly suspect there are more wavelet techniques used in finance than are listed here, but we're kind of restricted to the research they've done.
On the other hand, when I'm thinking about a problem in denoising or non parametric regression, paging through this book is often useful, or will at least point me towards a solution. The chapter on Kalman filtering is extremely well written, and the chapter on Neural Nets is pretty good too. I'd have rather the Kalman chapter had more on, say, unscented Kalman and particle filters, and I'd rather the NN chapter were on Kernel regression (which it does mention), but I'm reasonably happy with what was in there. Sure, it's kind of ad-hoc that a book on wavelets has chapters on Kalman filters and Neural Nets, but they're decent chapters worth having anyway. Everyone I know in a certain part of the finance business has this book, so it does serve a role. I would have liked more on wavelets, while keeping the idiosyncratic chapters, and perhaps more R packages.
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