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 17
... input—that is, CX) y =X-wix-i. (2.3) i=0 where only the past and present values of input are utilized. A filter with this property is called a causal filter or a physically realizable filter. If the filter coefficients are constant over ...
... input—that is, CX) y =X-wix-i. (2.3) i=0 where only the past and present values of input are utilized. A filter with this property is called a causal filter or a physically realizable filter. If the filter coefficients are constant over ...
Página 18
... input xt, as well as the current value of the input, are employed to determine the current value of output. In other words, there is a feedback from the past filter outputs to the current filter output. A difference equation in this ...
... input xt, as well as the current value of the input, are employed to determine the current value of output. In other words, there is a feedback from the past filter outputs to the current filter output. A difference equation in this ...
Página 19
... input in Equation 2.4, the impulse response of the filter is yO = x0 = 1 y1 = ay0 + 0 = a y2 = ay, 4-0 = a” y = a'. The filter has an impulse response with infinite duration. Therefore, it is an infinite impulse response (IIR) filter ...
... input in Equation 2.4, the impulse response of the filter is yO = x0 = 1 y1 = ay0 + 0 = a y2 = ay, 4-0 = a” y = a'. The filter has an impulse response with infinite duration. Therefore, it is an infinite impulse response (IIR) filter ...
Página 20
... input are required to obtain the filter output at time t, M data points at the beginning and N data points at the end of the output are missing. This feature makes a centered moving average less attractive in practice, in particular ...
... input are required to obtain the filter output at time t, M data points at the beginning and N data points at the end of the output are missing. This feature makes a centered moving average less attractive in practice, in particular ...
Página 21
... input x1, the first step is to filter the input by using the first set of filter coefficients w1: M Zf E. XD. 101,i Xt—i. i=-N At the second stage, the first stage filter output z is filtered once again by using the second set of filter ...
... input x1, the first step is to filter the input by using the first set of filter coefficients w1: M Zf E. XD. 101,i Xt—i. i=-N At the second stage, the first stage filter output z is filtered once again by using the second set of filter ...
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