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 44
... studied the 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 ...
... studied the 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 ...
Página 46
... studied in Burns and Mitchell (1946), which defines the duration of a business cycle from more than 1 year to 10 (or 12) years. The BK filter is also a centered moving average with symmetric weights; that is, R X = XL wiy—i. (2.37) i=–K ...
... studied in Burns and Mitchell (1946), which defines the duration of a business cycle from more than 1 year to 10 (or 12) years. The BK filter is also a centered moving average with symmetric weights; that is, R X = XL wiy—i. (2.37) i=–K ...
Página 54
... studied several examples offilteringingeneral and the Kalman filter in particular. Surveys by Engle and Watson (1987) and Hamilton (1994a) provided some examples of applying the Kalman filter in economics. We will start with the optimum ...
... studied several examples offilteringingeneral and the Kalman filter in particular. Surveys by Engle and Watson (1987) and Hamilton (1994a) provided some examples of applying the Kalman filter in economics. We will start with the optimum ...
Página 62
... studied. To illustrate how this may lead to a distorted picture, Figure 3.1 plots the true autocorrelation coefficients and the sample autocorrelation coefficients for the following AR(1) process: .xt = 0.90xt-1 + et, t = 0, 1 ...
... studied. To illustrate how this may lead to a distorted picture, Figure 3.1 plots the true autocorrelation coefficients and the sample autocorrelation coefficients for the following AR(1) process: .xt = 0.90xt-1 + et, t = 0, 1 ...
Página 100
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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