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.
|
Dentro del libro
Resultados 6-10 de 76
Página 25
... length R centered at O as in Figure 2.4. Let the radius rotate counterclockwise, starting from OA, moving toward OB. When it is back to OA, one cycle is completed. In a specific position such as OP, the radius makes aspecific angle 6 ...
... length R centered at O as in Figure 2.4. Let the radius rotate counterclockwise, starting from OA, moving toward OB. When it is back to OA, one cycle is completed. In a specific position such as OP, the radius makes aspecific angle 6 ...
Página 26
... length to be equal to the radius R. A radian is the size of the angle 6 formed by such an R-length arc. Since the circumference of a circle is 2T R and a complete circle is 360°, the following relation holds between degrees and radians ...
... length to be equal to the radius R. A radian is the size of the angle 6 formed by such an R-length arc. Since the circumference of a circle is 2T R and a complete circle is 360°, the following relation holds between degrees and radians ...
Página 27
... length. However, as we mentioned earlier, they have different starting values at OA where 6 = 0. From Figure 2.5 we see that if the cosine curve is shifted slightly to the right, an identical curve with the sine function is obtained ...
... length. However, as we mentioned earlier, they have different starting values at OA where 6 = 0. From Figure 2.5 we see that if the cosine curve is shifted slightly to the right, an identical curve with the sine function is obtained ...
Página 28
... length (period) f = 1/p, where f is the number of cycles per unit time. Accordingly, Equation 2.7 may be written by xt = sin (a + 27 ft), t = 0, 1, 2, ... N – 1. For instance, a monthly macroeconomic time series may have a seasonality ...
... length (period) f = 1/p, where f is the number of cycles per unit time. Accordingly, Equation 2.7 may be written by xt = sin (a + 27 ft), t = 0, 1, 2, ... N – 1. For instance, a monthly macroeconomic time series may have a seasonality ...
Página 29
... length of a cycle would be two time periods." To illustrate the Nyquist frequency, consider a sample from a continuous signal with a cyclical component with frequency f = 1/p, a cycle completing itself in p time periods. If the sampling ...
... length of a cycle would be two time periods." To illustrate the Nyquist frequency, consider a sample from a continuous signal with a cyclical component with frequency f = 1/p, a cycle completing itself in p time periods. If the sampling ...
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