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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... associated with coefficients at different scales or within their "Wavelets are in fact related to band-pass filters with properties similar to those used in the business-cycle literature for years. scale. This is convenient when ...
... associated with coefficients at different scales or within their "Wavelets are in fact related to band-pass filters with properties similar to those used in the business-cycle literature for years. scale. This is convenient when ...
Página 5
... associated with lower frequency oscillations (i.e., it is relatively smooth). Let us also assume that the noise process et is a sequence of uncorrelated Gaussian random variables with zero mean and variance o'. If we want the ...
... associated with lower frequency oscillations (i.e., it is relatively smooth). Let us also assume that the noise process et is a sequence of uncorrelated Gaussian random variables with zero mean and variance o'. If we want the ...
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... associated with high-frequency content of the time series) should retain this sudden shift in variability while the high-level coefficients should be stationary. * If the structural break of interest is a possible change in the long ...
... associated with high-frequency content of the time series) should retain this sudden shift in variability while the high-level coefficients should be stationary. * If the structural break of interest is a possible change in the long ...
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... associated with a particular time period. For instance, the first wavelet scale is associated with 20-min changes, the second wavelet scale with 40-min changes, the third wavelet scale with 160-min changes, and so on. The seventh ...
... associated with a particular time period. For instance, the first wavelet scale is associated with 20-min changes, the second wavelet scale with 40-min changes, the third wavelet scale with 160-min changes, and so on. The seventh ...
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... (associated with oscillations of 16 to 32 months), the DEM-USD exchange rate is negatively correlated with the JPY-USD at a lag of +8 months but not at a lag of –8 months. This feature, which reveals important information about the lead ...
... (associated with oscillations of 16 to 32 months), the DEM-USD exchange rate is negatively correlated with the JPY-USD at a lag of +8 months but not at a lag of –8 months. This feature, which reveals important information about the lead ...
Contenido
1 | |
15 | |
CHAPTER 3 OPTIMUM LINEAR ESTIMATION | 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