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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Dentro del libro
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Página ix
... Properties of the Wavelet Transform 4.2. I Continuous Wavelet Functions 4.2.2 Continuous versus Discrete Wavelet Transform Discrete Wavelet Filters 4.3. I Haar Wavelets 4.3.2. Daubechies Wavelets 4.3.3 Minimum Bandwidth Discrete-Time ...
... Properties of the Wavelet Transform 4.2. I Continuous Wavelet Functions 4.2.2 Continuous versus Discrete Wavelet Transform Discrete Wavelet Filters 4.3. I Haar Wavelets 4.3.2. Daubechies Wavelets 4.3.3 Minimum Bandwidth Discrete-Time ...
Página 1
... properties of a process. It is important to realize that economic/financial time series may not need to follow the same relationship as a function of time horizon (scale). Hence, a transform that decomposes a process into different time ...
... properties of a process. It is important to realize that economic/financial time series may not need to follow the same relationship as a function of time horizon (scale). Hence, a transform that decomposes a process into different time ...
Página 28
... properties of a sinusoidal signal. Consider the following time series: *-*( : '). t = 0, 1, 2, ... N – 1, (2.7) p where each oscillation completes itself in p time periods so that there are N/p oscillations. When p = N, there is only ...
... properties of a sinusoidal signal. Consider the following time series: *-*( : '). t = 0, 1, 2, ... N – 1, (2.7) p where each oscillation completes itself in p time periods so that there are N/p oscillations. When p = N, there is only ...
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... squared magnitude of the Fourier transform (spectrum) of the series as a function of the frequency to investigate the energy properties of the signal in frequency domain. 4 I I I I t I p j I 2.3 FILTERS IN THE FREQUENCY DOMAIN 3|
... squared magnitude of the Fourier transform (spectrum) of the series as a function of the frequency to investigate the energy properties of the signal in frequency domain. 4 I I I I t I p j I 2.3 FILTERS IN THE FREQUENCY DOMAIN 3|
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... properties of the filter. The squared gain function of a filter is similar to the spectral density function of stationary time series; only the filter is a deterministic function, while the time series is stochastic. *Total Production ...
... properties of the filter. The squared gain function of a filter is similar to the spectral density function of stationary time series; only the filter is a deterministic function, while the time series is stochastic. *Total Production ...
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