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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Página 13
... discrete wavelet transform (DWT). Most of the emphasis in Chapter 4 is placed on discrete wavelet functions (such as those of Daubechies) and implementing the DWT, decomposition of variance and multiresolution analysis (MRA) being two ...
... discrete wavelet transform (DWT). Most of the emphasis in Chapter 4 is placed on discrete wavelet functions (such as those of Daubechies) and implementing the DWT, decomposition of variance and multiresolution analysis (MRA) being two ...
Página 16
... discrete time series is a sequence of observations ordered by a time index t, where time spans from minus infinity to plus infinity, (*):-2. : (. . . , X-2, X-1, X0, X1, X2, . . .). An observed time series vector x offinite length may ...
... discrete time series is a sequence of observations ordered by a time index t, where time spans from minus infinity to plus infinity, (*):-2. : (. . . , X-2, X-1, X0, X1, X2, . . .). An observed time series vector x offinite length may ...
Página 28
... , ... N – 1. "Notice that if the period length of a cycle is greater than the sample size, p > N, only a portion of the cycle is observed. In discrete time setting, the frequency f = 1/2, or 28 CHAPTER 2 LINEAR FILTERS.
... , ... N – 1. "Notice that if the period length of a cycle is greater than the sample size, p > N, only a portion of the cycle is observed. In discrete time setting, the frequency f = 1/2, or 28 CHAPTER 2 LINEAR FILTERS.
Página 29
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. In discrete time setting, the frequency f = 1/2, or the angular frequency a) = T, is known as a Nyquist frequency, which is the highest possible frequency since the shortest length of a ...
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. In discrete time setting, the frequency f = 1/2, or the angular frequency a) = T, is known as a Nyquist frequency, which is the highest possible frequency since the shortest length of a ...
Página 30
... discrete-time frequencies. The squared magnitude of the Fourier transform |X(f ) is known as the energydensity spectrum or the power spectrum of the signal xt (Oppenheim and Schafer, 1989, Chapter 2). The Fourier representation of a ...
... discrete-time frequencies. The squared magnitude of the Fourier transform |X(f ) is known as the energydensity spectrum or the power spectrum of the signal xt (Oppenheim and Schafer, 1989, Chapter 2). The Fourier representation of a ...
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 |
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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