An Introduction to Wavelets and Other Filtering Methods in Finance and EconomicsAn 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 multiresolution 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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... Cyclical 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 ...
... Cyclical 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 ...
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The resulting expansion is a function of two parameters: frequency and time shift. Since the STFT is simply applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to ...
The resulting expansion is a function of two parameters: frequency and time shift. Since the STFT is simply applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to ...
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If the structural break of interest is a sudden change in variance, then the lowlevel wavelet coefficients (which are associated with highfrequency content of the time series) should retain this sudden shift in variability while the ...
If the structural break of interest is a sudden change in variance, then the lowlevel wavelet coefficients (which are associated with highfrequency content of the time series) should retain this sudden shift in variability while the ...
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Furthermore, wavelet methods are localized in time so that they can easily identify nonstationary events, such as sudden regime shifts and transient shocks to a system. Figure 1.6 illustrates such a decomposition by separating the ...
Furthermore, wavelet methods are localized in time so that they can easily identify nonstationary events, such as sudden regime shifts and transient shocks to a system. Figure 1.6 illustrates such a decomposition by separating the ...
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The required shift is ty2. These two curves are said to differ in phase (i.e., the location of the peak and the trough of the oscillation in each function is different in each period). 27 to any angle 6 would not change the value of sin ...
The required shift is ty2. These two curves are said to differ in phase (i.e., the location of the peak and the trough of the oscillation in each function is different in each period). 27 to any angle 6 would not change the value of sin ...
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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 
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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 beta calculated components computed correlation covariance cycle decomposition defined determined difference discrete distribution dynamics Equation error estimator example feedforward network Figure Fourier transform frequency function gain function Gaussian given Haar hidden units increases indicate input interval Kalman filter 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 values variables variance vector volatility wavelet coefficients wavelet details wavelet filter wavelet scale wavelet transform wavelet variance weights zero