Introduction to Wavelets and Other Filtering Methods in Finance and Economics (eBook)

Ramazan Gencay, Faruk Selcuk, Brandon J. Whitcher (Autoren)

eBook Download: EPUB

2001 | 1. Auflage
359 Seiten
Elsevier Science (Verlag)
978-0-08-050922-8 (ISBN)

Infos

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

Inhalt

Front Cover 1
AN INTRODUCTION TO WAVELETS AND OTHER FILTERING METHODS IN FINANCE AND ECONOMICS 4
Copyright Page 5
CONTENTS 8
DEDICATION 6
ACKNOWLEDGMENTS 20
PREFACE 22
CHAPTER 1. INTRODUCTION 24
1.1 Fourier versus Wavelet Analysis 25
1.2 Seasonality Filtering 26
1.3 Denoising 28
1.4 Identification of Structural Breaks 29
1.5 Scaling 30
1.6 Aggregate Heterogeneity and Timescales 33
1.7 Multiscale Cross-Correlation 33
1.8 Outline 36
CHAPTER 2. LINEAR FILTERS 38
2.1 Introduction 38
2.2 Filters in Time Domain 39
2.3 Filters in the Frequency Domain 48
2.4 Filters in Practice 62
CHAPTER 3. OPTIMUM LINEAR ESTIMATION 74
3.1 Introduction 74
3.2 The Wiener Filter and Estimation 77
3.3 Recursive Filtering and the Kalman Filter 86
3.4 Prediction with the Kalman Filter 94
3.5 Vector Kalman Filter Estimation 97
3.6 Applications 103
CHAPTER 4. DISCRETE WAVELET TRANSFORMS 119
4.1 Introduction 119
4.2 Properties of the Wavelet Transform 124
4.3 Discrete Wavelet Filters 129
4.4 The Discrete Wavelet Transform 140
4.5 The Maximal Overlap Discrete Wavelet Transform 157
4.6 Practical Issues in Implementation 166
4.7 Applications 169
CHAPTER 5. WAVELETS AND STATIONARY PROCESSES 184
5.1 Introduction 184
5.2 Wavelets and Long-Memory Processes 185
5.3 Generalizations of the DWT and MODWT 199
5.4 Wavelets and Seasonal Long Memory 206
5.5 Applications 217
CHAPTER 6. WAVELET DENOISING 225
6.1 Introduction 225
6.2 Nonlinear Denoising via Thresholding 230
6.3 Threshold Selection 235
6.4 Implementing Wavelet Denoising 247
6.5 Applications 250
CHAPTER 7. WAVELETS FOR VARIANCE-COVARIANCE ESTIMATION 258
7.1 Introduction 258
7.2 The Wavelet Variance 261
7.3 Testing Homogeneity of Variance 269
7.4 The Wavelet Covariance and Cross-Covariance 274
7.5 The Wavelet Correlation and Cross-Correlation 279
7.6 Applications 285
7.7 Univariate and Bivariate Spectrum Analysis 289
CHAPTER 8. ARTIFICIAL NEURAL NETWORKS 295
8.1 Introduction 295
8.2 Activation Functions 299
8.3 Feedforward Networks 302
8.4 Recurrent Networks 313
8.5 Network Selection 317
8.6 Adaptivity 324
8.7 Estimation of Recurrent Networks 324
8.8 Applications of Neural Network Models 326
NOTATIONS 338
BIBLIOGRAPHY 346
INDEX 372

Produkt-Details

• Erscheint lt. Verlag: 12.10.2001
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Sozialwissenschaften ► Pädagogik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
• ISBN-10: 0-08-050922-3 / 0080509223
• ISBN-13: 978-0-08-050922-8 / 9780080509228