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 57
... vector of optimum weights (coefficients), Try is the crosscovariance vector, and Tyy is the symmetric autocovariance matrix. Equation 3.11 (or Equation 3.12) is known as the Wiener-Hopf equation. If the autocovariance yyy and the cross ...
... vector of optimum weights (coefficients), Try is the crosscovariance vector, and Tyy is the symmetric autocovariance matrix. Equation 3.11 (or Equation 3.12) is known as the Wiener-Hopf equation. If the autocovariance yyy and the cross ...
Página 63
... vectors, x and y: N-li #, =# XD (x,-1)0-1–5), t=1 where x and y are the sample means. The cross-covariance sequence also satisfies the following constraint: N–1 X #, =0, (3.23) i=-(N-1) where £, is the estimated cross-covariance at ...
... vectors, x and y: N-li #, =# XD (x,-1)0-1–5), t=1 where x and y are the sample means. The cross-covariance sequence also satisfies the following constraint: N–1 X #, =0, (3.23) i=-(N-1) where £, is the estimated cross-covariance at ...
Página 74
... VECTOR KALN1AN FILTERESTIMATION The scalar Kalman filterestimator may easily be extended to a multivariate setting. Suppose that we haven different ... vector 74 CHAPTER 3 OPTIMUM LINEAR ESTIMATION 3.5 Vector Kalman Filter Estimation.
... VECTOR KALN1AN FILTERESTIMATION The scalar Kalman filterestimator may easily be extended to a multivariate setting. Suppose that we haven different ... vector 74 CHAPTER 3 OPTIMUM LINEAR ESTIMATION 3.5 Vector Kalman Filter Estimation.
Página 75
... vector autoregression (VAR) X = AX-1 + vi, (3.61) where X is a (k x 1) signal vector and A is a (k x k) coefficient 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 autoregression (VAR) X = AX-1 + vi, (3.61) where X is a (k x 1) signal vector and A is a (k x k) coefficient matrix, which describes the dynamics of the system. It is also called the system matrix. The system noise v1 is a (k x 1) ...
Página 76
... vector and the (k x k) system matrix A, is assumed to be known for all t.” The disturbance term v, is a (k x 1) serially uncorrelated vector with v. C. N(0, Q). The regression equation is Y, - Atat + €t, where Yi is an (n > 1 ...
... vector and the (k x k) system matrix A, is assumed to be known for all t.” The disturbance term v, is a (k x 1) serially uncorrelated vector with v. C. N(0, Q). The regression equation is Y, - Atat + €t, where Yi is an (n > 1 ...
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