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 30
... Equation 2.8 is also known as the synthesis equation since it represents the original sequence xt as a linear combination of complex sinusoids infinitesimally close in frequency with X(f) determining the relative weight of each complex ...
... Equation 2.8 is also known as the synthesis equation since it represents the original sequence xt as a linear combination of complex sinusoids infinitesimally close in frequency with X(f) determining the relative weight of each complex ...
Página 33
... Equation 2.16. In general, if the input is complex exponential in Equation 2.17, the general form of a linear filter in Equation 2.2 CQ y = XL. 10k Xt—k, k=-co becomes co y1 = XD. wei'"fo-k) k=-oo CQ ei2"ft. (. XL ..") 2 (2.19) k=-co where ...
... Equation 2.16. In general, if the input is complex exponential in Equation 2.17, the general form of a linear filter in Equation 2.2 CQ y = XL. 10k Xt—k, k=-co becomes co y1 = XD. wei'"fo-k) k=-oo CQ ei2"ft. (. XL ..") 2 (2.19) k=-co where ...
Página 35
... Equation 2.17 into Equation 2.21 results in. y. = 0.50e2"f. +0.50e”fo-0. (0.50. +0.50e-2"f. ) ei2+ft,. and the frequency response of this moving average is. H(f). = 0.50+0.50e"/. Notice that the frequency response is equal to one when f = 0 ...
... Equation 2.17 into Equation 2.21 results in. y. = 0.50e2"f. +0.50e”fo-0. (0.50. +0.50e-2"f. ) ei2+ft,. and the frequency response of this moving average is. H(f). = 0.50+0.50e"/. Notice that the frequency response is equal to one when f = 0 ...
Página 38
... equation) may be a high-pass or low-pass filter. Consider the following IIR filter, L M. XLaky-. -. XL. 10kW t—k, k=0 k=0 for which the frequency response is Y(f) M. —ik2tf. hop-'=#. X(f) 2}=0 Clké ik27tf where Y(f) and X(f) are the Fourier ...
... equation) may be a high-pass or low-pass filter. Consider the following IIR filter, L M. XLaky-. -. XL. 10kW t—k, k=0 k=0 for which the frequency response is Y(f) M. —ik2tf. hop-'=#. X(f) 2}=0 Clké ik27tf where Y(f) and X(f) are the Fourier ...
Página 39
... equation in Equation 2.25 with different parameter settings. The sign of the parameter a determines whether the linear difference equation is a low-pass filter as in the left panel or a high-pass filter as in the right panel. (a) a ...
... equation in Equation 2.25 with different parameter settings. The sign of the parameter a determines whether the linear difference equation is a low-pass filter as in the left panel or a high-pass filter as in the right panel. (a) a ...
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