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 35
... shift. The phase shift has important implications in economics and finance. Since a filter with a nonzero phase shifts the phase of the input, an analysis based on this type offilter would result in misspecification of turning points in ...
... shift. The phase shift has important implications in economics and finance. Since a filter with a nonzero phase shifts the phase of the input, an analysis based on this type offilter would result in misspecification of turning points in ...
Página 36
... 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.
... 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 37
... shift after filtering with an eight-period simple moving average. Notice that there is no phase shift in centered moving average output in (b). Data source: Datastream. sequence x = e”/": yt = |* + ei2"ft 12" : : (l + e-i2"f + *") ei2 ...
... shift after filtering with an eight-period simple moving average. Notice that there is no phase shift in centered moving average output in (b). Data source: Datastream. sequence x = e”/": yt = |* + ei2"ft 12" : : (l + e-i2"f + *") ei2 ...
Página 43
... shift Equation 2.30 one period back and multiply the resulting equation by (1 – A), we have (1–3)6 i = X(1-x)(r-1 – F)* + X(1–X)*(r-2-r) + . . . . (2,31) Subtracting Equation 2.31 from Equation 2.30 results in 6 = x(r. — F)* + (1–2)ó 1 ...
... shift Equation 2.30 one period back and multiply the resulting equation by (1 – A), we have (1–3)6 i = X(1-x)(r-1 – F)* + X(1–X)*(r-2-r) + . . . . (2,31) Subtracting Equation 2.31 from Equation 2.30 results in 6 = x(r. — F)* + (1–2)ó 1 ...
Página 44
... shift in the smooth after filtering. However, the filter in its original form is an infinite order moving average. Therefore, it cannot be implemented without making some restrictive assumptions about the lag length. A practical ...
... shift in the smooth after filtering. However, the filter in its original form is an infinite order moving average. Therefore, it cannot be implemented without making some restrictive assumptions about the lag length. A practical ...
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