An Introduction to Wavelets and Other Filtering Methods in Finance and Economics
- 1st Edition - September 12, 2001
- Latest edition
- Authors: Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher
- Language: English
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 financ… Read more
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
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.
Preface.
Notations.
1. Introduction
1.1 Fourier versus Wavelet Analysis
1.2 Seasonality Filtering
1.3 Denoising
1.4 Identification of Structural Breaks
1.5 Scaling
1.6 Aggregate Heterogeneity and Time Scales
1.7 Multiscale Cross-Correlation
1.8 Outline
2. Linear Filters
2.1 Introduction
2.2 Filters in Time Domain
2.3 Filters in the Frequency Domain
2.3 Filters in Practice
3. Optimum Linear Estimation
3.1 Introduction
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
3.6 Applications
4. Discrete Wavelet Transforms
4.1 Introduction
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
4.7 Applications
5. Wavelets and Stationary Processes
5.1 Introduction
5.2 Wavelets and Long-Memory Processes
5.3 Generalizations of the DWT and MODWT
5.4 Wavelets and Seasonal Long Memory
5.5 Applications
6. Wavelet Denoising
6.1 Introduction
6.2 Nonlinear Denoising via Thresholding
6.3 Threshold Selection
6.4 Implementing Wavelet Denoising
6.5 Applications
7. Wavelets for Variance-Covariance Estimation
7.1 Introduction
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
7.6 Applications
7.7 Univariate and Bivariate Spectrum Analysis
8. Artificial Neural Networks
8.1 Introduction
8.2 Activation Functions
8.3 Feedforward Networks
8.4 Recurrent Networks
8.5 Network Selection
8.6 Adaptivity
8.7 Estimation of Recurrent Networks
8.8 Applications of Neural Network Models
Notations
Bibliography
Index
Notations.
1. Introduction
1.1 Fourier versus Wavelet Analysis
1.2 Seasonality Filtering
1.3 Denoising
1.4 Identification of Structural Breaks
1.5 Scaling
1.6 Aggregate Heterogeneity and Time Scales
1.7 Multiscale Cross-Correlation
1.8 Outline
2. Linear Filters
2.1 Introduction
2.2 Filters in Time Domain
2.3 Filters in the Frequency Domain
2.3 Filters in Practice
3. Optimum Linear Estimation
3.1 Introduction
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
3.6 Applications
4. Discrete Wavelet Transforms
4.1 Introduction
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
4.7 Applications
5. Wavelets and Stationary Processes
5.1 Introduction
5.2 Wavelets and Long-Memory Processes
5.3 Generalizations of the DWT and MODWT
5.4 Wavelets and Seasonal Long Memory
5.5 Applications
6. Wavelet Denoising
6.1 Introduction
6.2 Nonlinear Denoising via Thresholding
6.3 Threshold Selection
6.4 Implementing Wavelet Denoising
6.5 Applications
7. Wavelets for Variance-Covariance Estimation
7.1 Introduction
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
7.6 Applications
7.7 Univariate and Bivariate Spectrum Analysis
8. Artificial Neural Networks
8.1 Introduction
8.2 Activation Functions
8.3 Feedforward Networks
8.4 Recurrent Networks
8.5 Network Selection
8.6 Adaptivity
8.7 Estimation of Recurrent Networks
8.8 Applications of Neural Network Models
Notations
Bibliography
Index
Pre-publication Reviews
"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, 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
Reviews
"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."—www.Wilmott.com
"...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."—MATHEMATICAL REVIEWS
- Edition: 1
- Latest edition
- Published: September 12, 2001
- Language: English
RG
Ramazan Gençay
Ramazan Gençay is a professor in the economics department at Simon Fraser University. His areas of specialization are financial econometrics, nonlinear time series, nonparametric econometrics, and chaotic dynamics. His publications appear in finance, economics, statistics and physics journals. His work has appeared in the Journal of the American Statistical Association, Journal of Econometrics, and Physics Letters A.
Affiliations and expertise
Simon Fraser University, Burnaby, British Columbia, CanadaFS
Faruk Selçuk
Faruk Selçuk is a faculty member in the department of economics at Bilkent University, Ankara, Turkey. His research interests are time series analysis, financial econometrics, risk management, emerging market economies, and the Turkish economy. His recent publications appeared in Studies in Nonlinear Dynamics and Econometrics, International Journal of Forecasting, and Physica A. He is a consultant for Reuters-Istanbul and Reuters-Moscow.
Affiliations and expertise
Bilkent University, Ankara, TurkeyBW
Brandon J. Whitcher
Brandon Whitcher is currently a visiting scientist in the Geophysical Statistics Project at the National Center for Atmospheric Research. He was a research scientist at EURANDOM, a European research institute for the study of stochastic phenomena, after receiving his Ph.D. in statistics from the University of Washington. His research interests include wavelet methodology, time series analysis, computational statistics, and applications in the physical sciences, finance, and economics. His publications have appeared in Exploration Geophysics, Journal of Computational and Graphical Statistics, Journal of Geophysical Research, Journal of Statistical Computation and Simulation, and Physica A.
Affiliations and expertise
National Center for Atmospheric Research, Boulder, Colorado, U.S.A.Read An Introduction to Wavelets and Other Filtering Methods in Finance and Economics on ScienceDirect