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 viii
... .3 Bayesian Vector Autoregression 3.6.4 Time-Varying Beta Estimation 15 16 18 19 22 25 31 34 39 40 44 46 48 51 54 58 59 60 61 63 65 67 71 72 73 74 76 77 78 78 79 80 80 83 84 90 4. I 4.2 4.3 4.4 4.5 4.6 4.7 4 DISCRETE CONTENTS.
... .3 Bayesian Vector Autoregression 3.6.4 Time-Varying Beta Estimation 15 16 18 19 22 25 31 34 39 40 44 46 48 51 54 58 59 60 61 63 65 67 71 72 73 74 76 77 78 78 79 80 80 83 84 90 4. I 4.2 4.3 4.4 4.5 4.6 4.7 4 DISCRETE CONTENTS.
Página ix
... Discrete Wavelet Filters 4.3. I Haar Wavelets 4.3.2. Daubechies Wavelets 4.3.3 Minimum Bandwidth Discrete-Time Wavelets The Discrete Wavelet Transform 4.4.1 Implementation of the DWT. Pyramid Algorithm 4.4.2 Partial Discrete Wavelet ...
... Discrete Wavelet Filters 4.3. I Haar Wavelets 4.3.2. Daubechies Wavelets 4.3.3 Minimum Bandwidth Discrete-Time Wavelets The Discrete Wavelet Transform 4.4.1 Implementation of the DWT. Pyramid Algorithm 4.4.2 Partial Discrete Wavelet ...
Página x
... Discrete Wavelet Packet Transform 5.3.2. The Maximal Overlap Discrete Wavelet Packet Transform 5.3.3 Example: Mexican Money Supply Wavelets and Seasonal Long Memory 5.4. | Seasonal Persistent Processes 5.4.2 Simulation of Seasonal ...
... Discrete Wavelet Packet Transform 5.3.2. The Maximal Overlap Discrete Wavelet Packet Transform 5.3.3 Example: Mexican Money Supply Wavelets and Seasonal Long Memory 5.4. | Seasonal Persistent Processes 5.4.2 Simulation of Seasonal ...
Página xiv
... discrete-time wavelet basis functions Time series decomposition via the DWT Time series reconstruction of the DWT Wavelet decompositions of the IBM return series Sum of squared wavelet coefficients for the IBM returns DWT ...
... discrete-time wavelet basis functions Time series decomposition via the DWT Time series reconstruction of the DWT Wavelet decompositions of the IBM return series Sum of squared wavelet coefficients for the IBM returns DWT ...
Página xvii
... discrete time wavelets Unit-root test results for raw and first differenced series Multiscale Granger causality tests Granger causality F-tests Maximum dynamic range for the spectra of DWT wavelet coefficients Minimax thresholds Results ...
... discrete time wavelets Unit-root test results for raw and first differenced series Multiscale Granger causality tests Granger causality F-tests Maximum dynamic range for the spectra of DWT wavelet coefficients Minimax thresholds Results ...
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 |
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