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 62
... matrix is N XX'.I' N–1) # . However, the sum of the ith row of the matrix is identically zero for all i, from which the stated result follows immediately (Percival, 1993). The result has important implications fortime series ...
... matrix is N XX'.I' N–1) # . However, the sum of the ith row of the matrix is identically zero for all i, from which the stated result follows immediately (Percival, 1993). The result has important implications fortime series ...
Página 73
... constant. Hy: , known as the golden ratio. The golden ratio appears regularly in nature as the ratio of the length to the width. 13 In practice, this matrix may not be known and 3.4, PREDICTION WITH THE KALMAN FILTER 73.
... constant. Hy: , known as the golden ratio. The golden ratio appears regularly in nature as the ratio of the length to the width. 13 In practice, this matrix may not be known and 3.4, PREDICTION WITH THE KALMAN FILTER 73.
Página 74
... matrix, which describes the relationship between the signals and observations. It is also called the observation matrix. The observation noise et is an (n > 1) vector with €161 €162 . . . 6 16m T €2é1 €262 . . . 626m E(et) = 0, E(ere ...
... matrix, which describes the relationship between the signals and observations. It is also called the observation matrix. The observation noise et is an (n > 1) vector with €161 €162 . . . 6 16m T €2é1 €262 . . . 626m E(et) = 0, E(ere ...
Página 75
... matrix, which describes the dynamics of the system. It is also called the system matrix. The system noise v1 is a (k x 1) vector with 1/1 W1 1/1 V2 . . . v1 vk T 1/21/1 V2 V2 . . . V2 vk E(vt) = 0, E(v; vi) = E • • = Q1. l/k 1/1 VK V2 ...
... matrix, which describes the dynamics of the system. It is also called the system matrix. The system noise v1 is a (k x 1) vector with 1/1 W1 1/1 V2 . . . v1 vk T 1/21/1 V2 V2 . . . V2 vk E(vt) = 0, E(v; vi) = E • • = Q1. l/k 1/1 VK V2 ...
Página 76
... matrix given via Equation 3.58 xx =Swc' [cswc' + Raj." • (3.63) Here, SN is the (k x k) error variance-covariance matrix, SN = APR-1A" + Qw, (3.64) and the (k x k) variance-covariance matrix of the estimation error is given by ? = SN ...
... matrix given via Equation 3.58 xx =Swc' [cswc' + Raj." • (3.63) Here, SN is the (k x k) error variance-covariance matrix, SN = APR-1A" + Qw, (3.64) and the (k x k) variance-covariance matrix of the estimation error is given by ? = SN ...
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