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| AN INTRODUCTION TO WAVELETS AND OTHER FILTERING METHODS IN FINANCE AND ECONOMICS
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To order this title, and for more information, click here
By
Ramazan Gençay, University of Windsor, Ontario, Canada
Faruk Selçuk, Bilkent University, Ankara, Turkey
Brandon Whitcher, National Center for Atmospheric Research, Boulder, Colorado, U.S.A.
Description
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.
Audience
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.
Contents
CONTENTS:
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
| Bibliographic details |
Hardbound, 359 pages, To order this title, and for more information, go to http://www.bh.com/apcatalog/default.asp?isbn=0122796705
To see more
Economics and Finance book titles please visit
http://www.elsevier.com/homepage/sae/econworld/econbooks.htm
elseviereconomics
, publication date: SEP-2001
ISBN-13: 978-0-12-279670-8
ISBN-10: 0-12-279670-5
Imprint: ACADEMIC PRESS
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Last update: 4 Sep 2009
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