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 1
... Wavelet filters provide an easy vehicle to study the multiresolution properties of a process. It is important to realize that economic/financial time series may not need to follow the same relationship as a function of time horizon (scale) ...
... Wavelet filters provide an easy vehicle to study the multiresolution properties of a process. It is important to realize that economic/financial time series may not need to follow the same relationship as a function of time horizon (scale) ...
Página 2
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. scale. This is convenient when performing tasks such as simulations, estimation, and testing since it is always easier to deal with an uncorrelated process as opposed to one with unknown ...
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. scale. This is convenient when performing tasks such as simulations, estimation, and testing since it is always easier to deal with an uncorrelated process as opposed to one with unknown ...
Página 9
... wavelet variances are plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the estimated variances for each wavelet scale and the straight lines are ordinary least squares (OLS) estimates. Each wavelet ...
... wavelet variances are plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the estimated variances for each wavelet scale and the straight lines are ordinary least squares (OLS) estimates. Each wavelet ...
Página 10
... scale-by-scale basis has the ability to unveil structure at different time horizons. For example, the wavelet transform produces an alternative, known as the wavelet variance, to the periodogram. This variance decomposition may be ...
... scale-by-scale basis has the ability to unveil structure at different time horizons. For example, the wavelet transform produces an alternative, known as the wavelet variance, to the periodogram. This variance decomposition may be ...
Página 11
... wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be expected since each increase in the scale captures lower and lower frequency content in the ...
... wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be expected since each increase in the scale captures lower and lower frequency content in 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