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 2
... Figure 1.1a introduces a square-wave function, based on the Haar wavelet filter, and a shifted version of the same function backward in time (Figure 1.1b). The wavelet filter is long in time when capturing low-frequency events (Figure ...
... Figure 1.1a introduces a square-wave function, based on the Haar wavelet filter, and a shifted version of the same function backward in time (Figure 1.1b). The wavelet filter is long in time when capturing low-frequency events (Figure ...
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... FIGURE 1.2 Sample autocorrelation function for the simulated AR(1) process (straight line), AR(I) plus seasonal process (dashed line), and wavelet smooth of the AR(1) plus seasonal process (dotted line). s 4 ... / 27tt yi = 0.95y;-1 + 2 ...
... FIGURE 1.2 Sample autocorrelation function for the simulated AR(1) process (straight line), AR(I) plus seasonal process (dashed line), and wavelet smooth of the AR(1) plus seasonal process (dotted line). s 4 ... / 27tt yi = 0.95y;-1 + 2 ...
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... Figure 1.2 the solid line is the ACF of the nonseasonal AR(1) dynamics and the dotted lines are the ACF of the seasonally adjusted series using a wavelet multiresolution analysis. As Figure 1.2 displays, using a multiresolution analysis ...
... Figure 1.2 the solid line is the ACF of the nonseasonal AR(1) dynamics and the dotted lines are the ACF of the seasonally adjusted series using a wavelet multiresolution analysis. As Figure 1.2 displays, using a multiresolution analysis ...
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... Figure 1.4. Since the level 1 coefficients (second row from the top in Figure 1.4) are associated with the highest frequencies, we use its maximum as the estimated time of variance change. Here the wavelet transform allowed for ...
... Figure 1.4. Since the level 1 coefficients (second row from the top in Figure 1.4) are associated with the highest frequencies, we use its maximum as the estimated time of variance change. Here the wavelet transform allowed for ...
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... FIGURE 1.5 Multiscale variance for 20-min absolute returns of (a) DEM-USD and (c) JPY-USD. In (a) and (c), the estimated wavelet variances are plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the ...
... FIGURE 1.5 Multiscale variance for 20-min absolute returns of (a) DEM-USD and (c) JPY-USD. In (a) and (c), the estimated wavelet variances are plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are 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 |
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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