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 49
Página 36
... a four-period phase shift. It is desirable to have a zero phase filter to preserve the phase properties of the input series. A centered moving average is an example of a zero phase (a) 105 - #| || 105 I f n. -. 36 CHAPTER 2. LINEAR FILTERS.
... a four-period phase shift. It is desirable to have a zero phase filter to preserve the phase properties of the input series. A centered moving average is an example of a zero phase (a) 105 - #| || 105 I f n. -. 36 CHAPTER 2. LINEAR FILTERS.
Página 44
... properties of the HP filter and showed that the cyclical component of the HP filter has the following frequency response function: 2 H(f, A) = £. (2.35) 1 + 4A [1 – cos(27tf)] King and Rebelo (1993) also showed that the smooth component ...
... properties of the HP filter and showed that the cyclical component of the HP filter has the following frequency response function: 2 H(f, A) = £. (2.35) 1 + 4A [1 – cos(27tf)] King and Rebelo (1993) also showed that the smooth component ...
Página 47
... properties and that its cyclical component fails to capture a significant fraction of the variability in business-cycle frequencies (Guay and St-Amant, 1997; Murray, 2001). Nevertheless, both the HP and BK filters have become standard ...
... properties and that its cyclical component fails to capture a significant fraction of the variability in business-cycle frequencies (Guay and St-Amant, 1997; Murray, 2001). Nevertheless, both the HP and BK filters have become standard ...
Página 51
... properties in the frequency domain and classified them according to their frequency responses as high-pass, low-pass, or band-pass filters. It was assumed that the input to the filter is readily available and the filter coefficients are ...
... properties in the frequency domain and classified them according to their frequency responses as high-pass, low-pass, or band-pass filters. It was assumed that the input to the filter is readily available and the filter coefficients are ...
Página 94
Alcanzaste el límite de visualización de este libro.
Alcanzaste el límite de visualización de este libro.
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