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 xxi
... applied sciences, such as engineering, physics, medicine, biology and oceanography. Regardless of one's profession, this book assumes a basic understanding of mathematics, including such topics as trigonometry, basic linear algrebra ...
... applied sciences, such as engineering, physics, medicine, biology and oceanography. Regardless of one's profession, this book assumes a basic understanding of mathematics, including such topics as trigonometry, basic linear algrebra ...
Página 2
... applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to resolve events when they happen to fall within the width of the window. To overcome the fixed time-frequency ...
... applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to resolve events when they happen to fall within the width of the window. To overcome the fixed time-frequency ...
Página 5
... applying the 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 ...
... applying the 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 ...
Página 7
... applied to each level of the transform instead of developing customized procedures to deal with each type of structural break. Figure 1.4 shows the results of testing the daily IBM volatility series (absolute returns) for a single ...
... applied to each level of the transform instead of developing customized procedures to deal with each type of structural break. Figure 1.4 shows the results of testing the daily IBM volatility series (absolute returns) for a single ...
Página 13
... applied to two types of stationary time series models: long-memory processes and seasonal long-memory processes. Wavelet-based simulation and estimation (both least squares and maximum likelihood) procedures are provided for these ...
... applied to two types of stationary time series models: long-memory processes and seasonal long-memory processes. Wavelet-based simulation and estimation (both least squares and maximum likelihood) procedures are provided for these ...
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