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 xiii
... functions Time series of representations of cyclical functions Fourier transform of a signal Gain function of an ideal filter Gain functions of simple moving averages Phase shift from a simple moving average Gain functions of a first ...
... functions Time series of representations of cyclical functions Fourier transform of a signal Gain function of an ideal filter Gain functions of simple moving averages Phase shift from a simple moving average Gain functions of a first ...
Página xiv
... function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet approximations Haar wavelet filter coefficients The Haar wavelet filter in frequency domain Daubechies wavelet filters of ...
... function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet approximations Haar wavelet filter coefficients The Haar wavelet filter in frequency domain Daubechies wavelet filters of ...
Página 32
... function of the period p = 1/f. Although it is difficult to identify any periodic component in (a), the Fourier transform in (b) clearly indicates that the relative weights of the components with ... gain function. 32 CHAPTER 2 LINEAR ...
... function of the period p = 1/f. Although it is difficult to identify any periodic component in (a), the Fourier transform in (b) clearly indicates that the relative weights of the components with ... gain function. 32 CHAPTER 2 LINEAR ...
Página 33
... gain function and it is the magnitude of the frequency response function, |H(f)]. The second part is the phase function. This decomposition would enable us to obtain the gain function G(f) and the phase angle 6 of a filter (Oppenheim ...
... gain function and it is the magnitude of the frequency response function, |H(f)]. The second part is the phase function. This decomposition would enable us to obtain the gain function G(f) and the phase angle 6 of a filter (Oppenheim ...
Página 34
... gain functions of an ideal filter (a) A high-pass filter capturing the frequency dynamics between f = 1/4 and f = 1 ... function of the frequency is known as again diagram.” If the gain is large at low frequencies and small at higher ...
... gain functions of an ideal filter (a) A high-pass filter capturing the frequency dynamics between f = 1/4 and f = 1 ... function of the frequency is known as again diagram.” If the gain is large at low frequencies and small at higher ...
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