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 22
... point centered moving average is actually a convolution of four filters (Chatfield, 1984, page 43). w = (0.25, 0.25, 0.25, 0.25) + (0.25, 0.25, 0.25, 0.25) + (0.20, 0.20, 0.20, 0.20, 0.20) + (–0.75, 0.75, 1, 0.75, −0.75). 2.2.3 Causal ...
... point centered moving average is actually a convolution of four filters (Chatfield, 1984, page 43). w = (0.25, 0.25, 0.25, 0.25) + (0.25, 0.25, 0.25, 0.25) + (0.20, 0.20, 0.20, 0.20, 0.20) + (–0.75, 0.75, 1, 0.75, −0.75). 2.2.3 Causal ...
Página 35
... points in the original time series. Figure 2.10 presents an example of the phase shift after filtering. The original time series is the U.S. Industrial Production Index (IPI)." The Business (a) t (b) 1 U.8 0.8 0.5 U.6 : &. *Total ...
... points in the original time series. Figure 2.10 presents an example of the phase shift after filtering. The original time series is the U.S. Industrial Production Index (IPI)." The Business (a) t (b) 1 U.8 0.8 0.5 U.6 : &. *Total ...
Página 39
... points. Therefore, an approximation to an ideal filter is used to extract the components of a time series in a particular frequency range, such as business cycles with known duration. We present four examples of filters in economics and ...
... points. Therefore, an approximation to an ideal filter is used to extract the components of a time series in a particular frequency range, such as business cycles with known duration. We present four examples of filters in economics and ...
Página 45
... points out that the HP filter fails in the task of generating a detrended series by allowing powerful low-frequency components to pass through into the detrended series. As noted by Baxter and King (1999), the HP filter also has some ...
... points out that the HP filter fails in the task of generating a detrended series by allowing powerful low-frequency components to pass through into the detrended series. As noted by Baxter and King (1999), the HP filter also has some ...
Página 47
... points are missing (plotted as zero) at the each end of the BK(6,32) filter output. Source: U.S. Department of Commerce, Bureau of Economic Analysis. 32 quarters by removing the trend variation, but it does not smooth out the ...
... points are missing (plotted as zero) at the each end of the BK(6,32) filter output. Source: U.S. Department of Commerce, Bureau of Economic Analysis. 32 quarters by removing the trend variation, but it does not smooth out the ...
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