An Introduction to Wavelets and Other Filtering Methods in Finance and Economics
- 1st Edition - September 12, 2001
- Imprint: Academic Press
- Authors: Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 3 9 9 5 5 6 - 8
- Hardback ISBN:9 7 8 - 0 - 1 2 - 2 7 9 6 7 0 - 8
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 5 0 9 2 2 - 8
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
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Request a sales quoteAn 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
- Edition: 1
- Published: September 12, 2001
- No. of pages (Hardback): 384
- No. of pages (eBook): 359
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780123995568
- Hardback ISBN: 9780122796708
- eBook ISBN: 9780080509228
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