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 25
... zero at OA (V = 0), becomes one at OB (V = R), zero at OC angle 6 are defined by sin 6 = #. cos. 2.3 FILTERS IN THE FREQUENCY DOMAIN FIGURE 2,4 A circle with radius R. The sine, cosine,. 2.3 FILTERS IN THE FREQUENCY DOMAIN 25 2.3 Filters ...
... zero at OA (V = 0), becomes one at OB (V = R), zero at OC angle 6 are defined by sin 6 = #. cos. 2.3 FILTERS IN THE FREQUENCY DOMAIN FIGURE 2,4 A circle with radius R. The sine, cosine,. 2.3 FILTERS IN THE FREQUENCY DOMAIN 25 2.3 Filters ...
Página 26
... zero at OB, minus one at OC and zero at OD. However, the two functions have different starting values at OA, where the cosine function takes the value of one whereas the sine function has a value of zero. The angle 6 is a ratio and it ...
... zero at OB, minus one at OC and zero at OD. However, the two functions have different starting values at OA, where the cosine function takes the value of one whereas the sine function has a value of zero. The angle 6 is a ratio and it ...
Página 31
... zero mean and unit variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200. Although there are two periodic ...
... zero mean and unit variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200. Although there are two periodic ...
Página 35
... zero, there will be a change in the phase of the original time series. This is known as the phase shift. The phase shift has important implications in economics and finance. Since a filter with a nonzero phase shifts the phase of the ...
... zero, there will be a change in the phase of the original time series. This is known as the phase shift. The phase shift has important implications in economics and finance. Since a filter with a nonzero phase shifts the phase of the ...
Página 36
... 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.
... 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.
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