An Introduction to Wavelets and Other Filtering Methods in Finance and EconomicsElsevier, 2001 M10 12 - 359 páginas 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.
|
Dentro del libro
Resultados 1-5 de 88
Página viii
... Frequency Domain 2.3.1 Frequency Response 2.3.2. Low-Pass and High-Pass Filters Filters in Practice 2.4.1 The EWMA and Volatility Estimation 2.4.2 The Hodrick-Prescott Filter 2.4.3 The Baxter-King (BK) Filter 2.4.4 Filters in Technical ...
... Frequency Domain 2.3.1 Frequency Response 2.3.2. Low-Pass and High-Pass Filters Filters in Practice 2.4.1 The EWMA and Volatility Estimation 2.4.2 The Hodrick-Prescott Filter 2.4.3 The Baxter-King (BK) Filter 2.4.4 Filters in Technical ...
Página xiv
... frequency plane Sine wave with jump discontinuity Morlet wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet ...
... frequency plane Sine wave with jump discontinuity Morlet wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet ...
Página xxii
... frequency series by decomposing the signal into its high- and low-frequency components. The focus of this book is descriptive and proofs are avoided as much as possible. This focus provides easy access to a wide spectrum of parametric ...
... frequency series by decomposing the signal into its high- and low-frequency components. The focus of this book is descriptive and proofs are avoided as much as possible. This focus provides easy access to a wide spectrum of parametric ...
Página 2
... frequency, and therefore the Fourier transform may be seen as a decomposition on a frequency-by-frequency basis. The Fourier basis functions (sines and cosines) are very appealing when working with stationary time series (see Section ...
... frequency, and therefore the Fourier transform may be seen as a decomposition on a frequency-by-frequency basis. The Fourier basis functions (sines and cosines) are very appealing when working with stationary time series (see Section ...
Página 3
... frequency dynamics. Specifically, the periodic component pulls the calculated autocorrelations down, giving the impression that there is . – AF1 * Wavele! Srriorith \ - AR1 = 0 50 100 150 200 250 300 0 50 100 150 200 250 300 no ...
... frequency dynamics. Specifically, the periodic component pulls the calculated autocorrelations down, giving the impression that there is . – AF1 * Wavele! Srriorith \ - AR1 = 0 50 100 150 200 250 300 0 50 100 150 200 250 300 no ...
Contenido
1 | |
15 | |
51 | |
CHAPTER 4 DISCRETE WAVELET TRANSFORMS | 96 |
CHAPTER 5 WAVELETS AND STATIONARY PROCESSES | 161 |
CHAPTER 6 WAVELET DENOISING | 202 |
CHAPTER 7 WAVELETS FOR VARIANCECOVARIANCE ESTIMATION | 235 |
CHAPTER 8 ARTIFICIAL NEURAL NETWORKS | 272 |
NOTATIONS | 315 |
BIBLIOGRAPHY | 323 |
INDEX | 349 |
Otras ediciones - Ver todas
An Introduction to Wavelets and Other Filtering Methods in Finance and Economics Ramazan Gençay,Faruk Selçuk,Brandon Whitcher Sin vista previa disponible - 2002 |
Términos y frases comunes
analysis applied approximate associated assumed basis calculated components computed correlation covariance cycle decomposition defined determined difference discrete distribution dynamics Equation error estimator example exchange feedforward network Figure Fourier transform frequency function gain function Gaussian given Haar hidden units increases indicate input interval known lags length linear matrix mean method MODWT moving average network model neural network noise observations obtained original output parameter performance period phase plotted points prediction presented procedure produce properties random recurrent respectively response returns rule sample scale seasonal sequence shift shows signal simple simulation smooth spectral spectrum squared standard stationary statistical studied term thresholding transform values variables variance vector volatility wavelet coefficients wavelet details wavelet filter wavelet scale wavelet transform wavelet variance weights zero