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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... MODWT 5.3. The 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 ...
... MODWT 5.3. The 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 ...
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... MODWT coefficients for the IBM returns MODWT multiresolution analysis of the IBM volatility Sample ACF for a simulated AR(1) process Sample ACF for the 5-min absolute returns Sample ACF for MODWT filtered 5-min absolute returns MODWT ...
... MODWT coefficients for the IBM returns MODWT multiresolution analysis of the IBM volatility Sample ACF for a simulated AR(1) process Sample ACF for the 5-min absolute returns Sample ACF for MODWT filtered 5-min absolute returns MODWT ...
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... MODWT multiresolution analysis of Austrian IPI and nominal money supply Monthly core inflation in some high-inflation economies Spectral densities for fractional difference processes Spectra for an FDP(0.4) and Haar wavelet coefficients ...
... MODWT multiresolution analysis of Austrian IPI and nominal money supply Monthly core inflation in some high-inflation economies Spectral densities for fractional difference processes Spectra for an FDP(0.4) and Haar wavelet coefficients ...
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... (MODWT), is also introduced. Several examples illustrate the use of the DWT and MODWT with respect to filtering foreign exchange rates, multiscale causality and cointegration, and the multiscale relationship between money growth and ...
... (MODWT), is also introduced. Several examples illustrate the use of the DWT and MODWT with respect to filtering foreign exchange rates, multiscale causality and cointegration, and the multiscale relationship between money growth and ...
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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