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.
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Dentro del libro
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Página xiv
... Example time series of sinusoids Partitioning of the time-frequency plane Sine wave with jump discontinuity Morlet wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency plane Squared ...
... Example time series of sinusoids Partitioning of the time-frequency plane Sine wave with jump discontinuity Morlet wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency plane Squared ...
Página xv
... Example functions for wavelet denoising Normalized cumulative sum of squares (NCSS) Approximations to the IBM volatility series Hard and soft thresholding rules Firm and nn-garrote thresholding rule The n-degree garrote thresholding ...
... Example functions for wavelet denoising Normalized cumulative sum of squares (NCSS) Approximations to the IBM volatility series Hard and soft thresholding rules Firm and nn-garrote thresholding rule The n-degree garrote thresholding ...
Página 5
... example. |.3 DENOISING A convenient model for a uniformly sampled process yt is that of the standard signal plus noise model; that is, yi = St + ét, t = 0, 1, . . . , N – 1. (1.2) For now letus assumes, is a deterministic function of t ...
... example. |.3 DENOISING A convenient model for a uniformly sampled process yt is that of the standard signal plus noise model; that is, yi = St + ét, t = 0, 1, . . . , N – 1. (1.2) For now letus assumes, is a deterministic function of t ...
Página 6
... example function st (N = 1024) with additive noise. The true function (dotted line) is drawn in the bottom two ... example of what is known as a structural break. Suppose we would like to test for homogeneity of variance for an observed ...
... example function st (N = 1024) with additive noise. The true function (dotted line) is drawn in the bottom two ... example of what is known as a structural break. Suppose we would like to test for homogeneity of variance for an observed ...
Página 9
... example, the first wavelet scale is associated with changes at 20-min, the second wavelet scale is associated with 40-min changes, and so on. An apparent break in the scaling law is observed in the variance at the seventh wavelet scale ...
... example, the first wavelet scale is associated with changes at 20-min, the second wavelet scale is associated with 40-min changes, and so on. An apparent break in the scaling law is observed in the variance at the seventh wavelet scale ...
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