An Introduction to Wavelets and Other Filtering Methods in Finance and Economics


  • Ramazan Gençay, Simon Fraser University, Burnaby, British Columbia, Canada
  • Ramazan Gençay, Simon Fraser University, Burnaby, British Columbia, Canada
  • Faruk Selçuk, Bilkent University, Ankara, Turkey
  • Brandon Whitcher, National Center for Atmospheric Research, Boulder, Colorado, U.S.A.

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.
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Upper division undergraduate and graduate students as well as professionals in economics and finance. Courses include econometrics, applied economic analysis, economic statistics, and probability and statistics.


Book information

  • Published: September 2001
  • ISBN: 978-0-12-279670-8


"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."
Prepublication Reviews, --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, Gencay, Selcuk, 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 underst and 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
"This book sells itself short by being called "An Introduction..." OK, so it does start at the ground floor, but this is one skyscraper of a book. Without any reservations we give it the thumbs up."
Reviews, —
"...the book is a stimulating introduction which [will] induce the reader to further development and application of the wavelets and the neural networks in the fields of econometrics and finance."

Table of Contents

CONTENTS:Preface.Notations.1. Introduction1.1 Fourier versus Wavelet Analysis1.2 Seasonality Filtering1.3 Denoising1.4 Identification of Structural Breaks1.5 Scaling1.6 Aggregate Heterogeneity and Time Scales1.7 Multiscale Cross-Correlation1.8 Outline2. Linear Filters2.1 Introduction2.2 Filters in Time Domain2.3 Filters in the Frequency Domain2.3 Filters in Practice3. Optimum Linear Estimation3.1 Introduction3.2 The Wiener Filter and Estimation3.3 Recursive Filtering and the Kalman Filter3.4 Prediction with the Kalman Filter3.5 Vector Kalman Filter Estimation3.6 Applications4. Discrete Wavelet Transforms4.1 Introduction4.2 Properties of the Wavelet Transform4.3 Discrete Wavelet Filters4.4 The Discrete Wavelet Transform4.5 The Maximal Overlap Discrete Wavelet Transform4.6 Practical Issues in Implementation4.7 Applications5. Wavelets and Stationary Processes5.1 Introduction5.2 Wavelets and Long-Memory Processes5.3 Generalizations of the DWT and MODWT5.4 Wavelets and Seasonal Long Memory5.5 Applications6. Wavelet Denoising6.1 Introduction6.2 Nonlinear Denoising via Thresholding6.3 Threshold Selection6.4 Implementing Wavelet Denoising6.5 Applications7. Wavelets for Variance-Covariance Estimation7.1 Introduction7.2 The Wavelet Variance7.3 Testing Homogeneity of Variance7.4 The Wavelet Covariance and Cross-Covariance7.5 The Wavelet Correlation and Cross-Correlation7.6 Applications7.7 Univariate and Bivariate Spectrum Analysis8. Artificial Neural Networks8.1 Introduction8.2 Activation Functions8.3 Feedforward Networks8.4 Recurrent Networks8.5 Network Selection8.6 Adaptivity8.7 Estimation of Recurrent Networks8.8 Applications of Neural Network ModelsNotationsBibliographyIndex