An introduction to wavelets and other filtering methods in finance and economics
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 ex...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
San Diego, CA :
Academic Press
2002.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627612106719 |
Tabla de Contenidos:
- Front Cover; AN INTRODUCTION TO WAVELETS AND OTHER FILTERING METHODS IN FINANCE AND ECONOMICS; Copyright Page; DEDICATION; CONTENTS; ACKNOWLEDGMENTS; PREFACE; CHAPTER 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 Timescales; 1.7 Multiscale Cross-Correlation; 1.8 Outline; CHAPTER 2. LINEAR FILTERS; 2.1 Introduction; 2.2 Filters in Time Domain; 2.3 Filters in the Frequency Domain; 2.4 Filters in Practice; CHAPTER 3. OPTIMUM LINEAR ESTIMATION; 3.1 Introduction
- 3.2 The Wiener Filter and Estimation3.3 Recursive Filtering and the Kalman Filter; 3.4 Prediction with the Kalman Filter; 3.5 Vector Kalman Filter Estimation; 3.6 Applications; CHAPTER 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; CHAPTER 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 Memory5.5 Applications; CHAPTER 6. WAVELET DENOISING; 6.1 Introduction; 6.2 Nonlinear Denoising via Thresholding; 6.3 Threshold Selection; 6.4 Implementing Wavelet Denoising; 6.5 Applications; CHAPTER 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; CHAPTER 8. ARTIFICIAL NEURAL NETWORKS; 8.1 Introduction
- 8.2 Activation Functions8.3 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
