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 51
... uncorrelated and distributed with zero. y = x + et, (3.2) | This assumption will be relaxed in Section 3.2. 2A sequence of uncorrelated random variables with zero mean and 5 | CHAPTER 3. OPTIMUM LINEAR ESTIMATION 3.1 Introduction.
... uncorrelated and distributed with zero. y = x + et, (3.2) | This assumption will be relaxed in Section 3.2. 2A sequence of uncorrelated random variables with zero mean and 5 | CHAPTER 3. OPTIMUM LINEAR ESTIMATION 3.1 Introduction.
Página 52
... variables with zero mean and constant variance is referred to as a white noise process. If the elements of this process are also independent across time, then it is called an independent white noise. Finally, if an independent white ...
... variables with zero mean and constant variance is referred to as a white noise process. If the elements of this process are also independent across time, then it is called an independent white noise. Finally, if an independent white ...
Página 55
... . ”Two vectors, a and b, are orthogonal if and only if a" b = b a = 0. Two random variables x and y are said to be orthogonal if E(xy) = 0. observation discarded: * 1 1 JC2 = 2* + 5)2. 3.2. THE WIENER FILTER AND ESTIMATION 55.
... . ”Two vectors, a and b, are orthogonal if and only if a" b = b a = 0. Two random variables x and y are said to be orthogonal if E(xy) = 0. observation discarded: * 1 1 JC2 = 2* + 5)2. 3.2. THE WIENER FILTER AND ESTIMATION 55.
Página 56
... variable x into a centered random variable x = x – E(x) to make the above autocovariance definition operational as long as we know the first moment of the variable E(x). yyy(0) yyy(1) • * * yyy(N – 1) w; yyy(1) 56 CHAPTER 3 OPTIMUM ...
... variable x into a centered random variable x = x – E(x) to make the above autocovariance definition operational as long as we know the first moment of the variable E(x). yyy(0) yyy(1) • * * yyy(N – 1) w; yyy(1) 56 CHAPTER 3 OPTIMUM ...
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... variables is given by '(W = p) = w W. According to the Wiener-Hopf equation (Equations 3.11, 3.12), we have to find the following: yyy = E(WW) = E(z+u)(z+u) = E(u°) + E(v*) }^xy E [W(W – p)] E(z+u)(u – v) = E(u”). Substituting these ...
... variables is given by '(W = p) = w W. According to the Wiener-Hopf equation (Equations 3.11, 3.12), we have to find the following: yyy = E(WW) = E(z+u)(z+u) = E(u°) + E(v*) }^xy E [W(W – p)] E(z+u)(u – v) = E(u”). Substituting these ...
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