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 10
... variance, to the periodogram. This variance decomposition may be easily generalized for multivariate time series. Standard time-domain measures of association for multivariate time series (e.g., cross-covariance and cross-correlation) ...
... variance, to the periodogram. This variance decomposition may be easily generalized for multivariate time series. Standard time-domain measures of association for multivariate time series (e.g., cross-covariance and cross-correlation) ...
Página 13
... variance and multiresolution analysis (MRA) being two key features. An alternative version of the DWT, the maximal ... variance (for a univariate time series) or the covariance (for bivariate time series) using the wavelet transform. The ...
... variance and multiresolution analysis (MRA) being two key features. An alternative version of the DWT, the maximal ... variance (for a univariate time series) or the covariance (for bivariate time series) using the wavelet transform. The ...
Página 31
... variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200. Although there are two periodic components in the signal ...
... variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200. Although there are two periodic components in the signal ...
Página 51
... variance o', denoted by x ~ (l, o?)." Therefore, the observation of this signal at time t is where observation noise et is uncorrelated and distributed with zero. y = x + et, (3.2) | This assumption will be relaxed in Section 3.2. 2A ...
... variance o', denoted by x ~ (l, o?)." Therefore, the observation of this signal at time t is where observation noise et is uncorrelated and distributed with zero. y = x + et, (3.2) | This assumption will be relaxed in Section 3.2. 2A ...
Página 52
... variance, denoted by et ~ (0, o?).” Filtering in this context can be viewed as extracting (or estimating) x from the noisy observation yi. Given the observation set,” y = (y1, y2, y3, ... , yN), a linear estimate of x at time N may be ...
... variance, denoted by et ~ (0, o?).” Filtering in this context can be viewed as extracting (or estimating) x from the noisy observation yi. Given the observation set,” y = (y1, y2, y3, ... , yN), a linear estimate of x at time N may be ...
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