*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
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.
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.
An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, 1st Edition
1.1 Fourier versus Wavelet Analysis
1.2 Seasonality Filtering
1.4 Identification of Structural Breaks
1.6 Aggregate Heterogeneity and Time Scales
1.7 Multiscale Cross-Correlation
2. Linear Filters
2.2 Filters in Time Domain
2.3 Filters in the Frequency Domain
2.3 Filters in Practice
3. Optimum Linear Estimation
3.2 The Wiener Filter and Estimation
3.3 Recursive Filtering and the Kalman Filter
3.4 Prediction with the Kalman Filter
3.5 Vector Kalman Filter Estimation
4. Discrete Wavelet Transforms
4.2 Properties of the Wavelet Transform
4.3 Discrete Wavelet Filters
4.4 The Discrete Wavelet Transform
4.5 The Maximal Overlap Discrete Wavelet Transform
4.6 Practical Issues in Implementation
5. Wavelets and Stationary Processes
5.2 Wavelets and Long-Memory Processes
5.3 Generalizations of the DWT and MODWT
5.4 Wavelets and Seasonal Long Memory
6. Wavelet Denoising
6.2 Nonlinear Denoising via Thresholding
6.3 Threshold Selection
6.4 Implementing Wavelet Denoising
7. Wavelets for Variance-Covariance Estimation
7.2 The Wavelet Variance
7.3 Testing Homogeneity of Variance
7.4 The Wavelet Covariance and Cross-Covariance
7.5 The Wavelet Correlation and Cross-Correlation
7.7 Univariate and Bivariate Spectrum Analysis
8. Artificial Neural Networks
8.2 Activation Functions
8.3 Feedforward Networks
8.4 Recurrent Networks
8.5 Network Selection
8.7 Estimation of Recurrent Networks
8.8 Applications of Neural Network Models
Quotes and reviews
@qu:"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."
@source:--CLIVE W.J. GRANGER, Professor of Economics, University of California, San Diego
@qu:"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."
@source:--JAMES B. RAMSEY, Professor of Economics, New York University
@qu:"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."
@source:--PETER M. ROBINSON, Tooke Professor of Economic Science and Statistics and Leverhulme Research Professor, London School of Economics, U.K.
@qu:"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."
@source:--Halbert White, University of California, San Diego
@qu:"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."
@qu:"...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."