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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Dentro del libro
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Página vii
... Seasonality Filtering | 2. I 2.2 2.3 2.4 3.3 3.4 3.5 3.6 2. Denoising Identification of Structural Breaks Scaling Aggregate Heterogeneity and Timescales Multiscale Cross-Correlation Outline 10 13 5. I 5.2 Introduction Wavelets and Long ...
... Seasonality Filtering | 2. I 2.2 2.3 2.4 3.3 3.4 3.5 3.6 2. Denoising Identification of Structural Breaks Scaling Aggregate Heterogeneity and Timescales Multiscale Cross-Correlation Outline 10 13 5. I 5.2 Introduction Wavelets and Long ...
Página x
... Seasonal Long Memory 5.4. | Seasonal Persistent Processes 5.4.2 Simulation of Seasonal Persistent Processes 5.4.3 Basis Selection Procedures 5.4.4 Ordinary Least-Squares Estimation of Seasonal Persistent Processes 5.4.5 Approximate ...
... Seasonal Long Memory 5.4. | Seasonal Persistent Processes 5.4.2 Simulation of Seasonal Persistent Processes 5.4.3 Basis Selection Procedures 5.4.4 Ordinary Least-Squares Estimation of Seasonal Persistent Processes 5.4.5 Approximate ...
Página xxii
... seasonality, identification of trader behavior at different trading horizons, and the usefulness of wavelet methods for cross-correlation analysis of two high-frequency series by decomposing the signal into its high- and low-frequency ...
... seasonality, identification of trader behavior at different trading horizons, and the usefulness of wavelet methods for cross-correlation analysis of two high-frequency series by decomposing the signal into its high- and low-frequency ...
Página 3
... seasonality filtering, denoising, identification of structural breaks, scaling, separating observed data into timescales (so-called multiresolution analysis) and comparing multiple time ... SEASONALITY FILTERING 3 1.2 Seasonality Filtering.
... seasonality filtering, denoising, identification of structural breaks, scaling, separating observed data into timescales (so-called multiresolution analysis) and comparing multiple time ... SEASONALITY FILTERING 3 1.2 Seasonality Filtering.
Página 4
... seasonal process (dashed line), and wavelet smooth of the AR(1) plus seasonal process (dotted line). s 4 ... / 27tt yi = 0.95y;-1 + 2. [. S1m ( P ) + 09. + €t (1.1) for t = 1,..., N, Pl = 3, P2 = 4, P = 5, and P1 = 6. The process has ...
... seasonal process (dashed line), and wavelet smooth of the AR(1) plus seasonal process (dotted line). s 4 ... / 27tt yi = 0.95y;-1 + 2. [. S1m ( P ) + 09. + €t (1.1) for t = 1,..., N, Pl = 3, P2 = 4, P = 5, and P1 = 6. The process has ...
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 |
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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