UID:
almahu_9948025557002882
Format:
1 online resource (383 p.)
Edition:
1st edition
ISBN:
1-282-28479-7
,
9786612284793
,
0-08-050922-3
Content:
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 sp
Note:
Description based upon print version of record.
,
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
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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
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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
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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
,
English
Additional Edition:
ISBN 0-12-279670-5
Language:
English
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