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 xiv
... wavelet - Wavelets and the Gaussian probability density ... wavelet approximations Haar wavelet filter coefficients The Haar wavelet filter in frequency domain Daubechies wavelet filters of lengths Le {2, 4,8} LA(8) wavelet filter ...
... wavelet - Wavelets and the Gaussian probability density ... wavelet approximations Haar wavelet filter coefficients The Haar wavelet filter in frequency domain Daubechies wavelet filters of lengths Le {2, 4,8} LA(8) wavelet filter ...
Página xv
... coefficients Monthly percentage changes in money supply in Mexico Wavelet packet table of money supply for Mexico Spectral density functions for SPPs Adaptive basis selection for an SPP Spectra for an SPP(0.4, 1/12) and MB(16) wavelet ...
... coefficients Monthly percentage changes in money supply in Mexico Wavelet packet table of money supply for Mexico Spectral density functions for SPPs Adaptive basis selection for an SPP Spectra for an SPP(0.4, 1/12) and MB(16) wavelet ...
Página xvii
... wavelet coefficients Minimax thresholds Results of testing IBM stock volatility for homogeneity of variance Wavelet estimates of betas for different companies Out-of-sample MSPE of the S&P-500 call options Out-of-sample MSPE per ...
... wavelet coefficients Minimax thresholds Results of testing IBM stock volatility for homogeneity of variance Wavelet estimates of betas for different companies Out-of-sample MSPE of the S&P-500 call options Out-of-sample MSPE per ...
Página 5
... wavelet transform to y, and thresholding the wavelet coefficients with threshold V2O2N is a good strategy. Utilizing this threshold one may then remove (hard thresholding) or shrink toward zero (soft thresholding) wavelet coefficients ...
... wavelet transform to y, and thresholding the wavelet coefficients with threshold V2O2N is a good strategy. Utilizing this threshold one may then remove (hard thresholding) or shrink toward zero (soft thresholding) wavelet coefficients ...
Página 6
... wavelet-based approach is to test the wavelet coefficients on a level-by-level basis. Two possible scenarios are as follows: * If the structural break of interest is a sudden. |.4 IDENTIFICATION OF STRUCTURAL BREAKS Time (trading days ...
... wavelet-based approach is to test the wavelet coefficients on a level-by-level basis. Two possible scenarios are as follows: * If the structural break of interest is a sudden. |.4 IDENTIFICATION OF STRUCTURAL BREAKS Time (trading days ...
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