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 52
Página xiv
... decomposition via the DWT Time series reconstruction of the DWT Wavelet decompositions of the IBM return series Sum of squared wavelet coefficients for the IBM returns DWT multiresolution analysis of the IBM volatility series Phase ...
... decomposition via the DWT Time series reconstruction of the DWT Wavelet decompositions of the IBM return series Sum of squared wavelet coefficients for the IBM returns DWT multiresolution analysis of the IBM volatility series Phase ...
Página xv
... Decomposition of x into DWPT coefficients Decomposition of x into DWT coefficients Decomposition of x into MODWPT coefficients Monthly percentage changes in money supply in Mexico Wavelet packet table of money supply for Mexico Spectral ...
... Decomposition of x into DWPT coefficients Decomposition of x into DWT coefficients Decomposition of x into MODWPT coefficients Monthly percentage changes in money supply in Mexico Wavelet packet table of money supply for Mexico Spectral ...
Página xxii
... 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 and nonparametric ...
... 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 and nonparametric ...
Página 2
... 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 4.1.1 for a definition of a stationary time series). However ...
... 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 4.1.1 for a definition of a stationary time series). However ...
Página 5
... decomposition in an attempt to eliminate the noise from the signal. Inverting the wavelet transform yields a nonparametric estimate of the underlying signal st. Thresholding wavelet coefficients is appealing, since they capture ...
... decomposition in an attempt to eliminate the noise from the signal. Inverting the wavelet transform yields a nonparametric estimate of the underlying signal st. Thresholding wavelet coefficients is appealing, since they capture ...
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