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 18
... determine the current value of output. In other words, there is a feedback from the past filter outputs to the ... determined even if x, and the coefficient a are available, unless auxiliary information about y, at some time to for ...
... determine the current value of output. In other words, there is a feedback from the past filter outputs to the ... determined even if x, and the coefficient a are available, unless auxiliary information about y, at some time to for ...
Página 20
... determined according to the successive terms from the expansionin (0.5 +0.5)*. For instance, when N = 1, the filter is y1 = 0.25xt_1 + 0.50xt + 0.25xt+1. This is known as the Hanning filter, after Julius Von. 20 CHAPTER 2 LINEAR FILTERS.
... determined according to the successive terms from the expansionin (0.5 +0.5)*. For instance, when N = 1, the filter is y1 = 0.25xt_1 + 0.50xt + 0.25xt+1. This is known as the Hanning filter, after Julius Von. 20 CHAPTER 2 LINEAR FILTERS.
Página 24
... determined according to the following second-order polynomial: w = to + bi – pai', i = 0, 1,... M, where po, p1 and p2 are positive constants between 0 and 1. The filter coefficients in this case are " – u)0 = $0 wi = to + (1) bi – (1 ...
... determined according to the following second-order polynomial: w = to + bi – pai', i = 0, 1,... M, where po, p1 and p2 are positive constants between 0 and 1. The filter coefficients in this case are " – u)0 = $0 wi = to + (1) bi – (1 ...
Página 29
... determined from Euler's relation, i8 e” = cosé + i sin 6. See Chiang (1984, Section 15.2) for a detailed exposition of the complex numbers. 11 An exponential signal x = Ae" may have real 2.3 FILTERS IN THE FREQUENCY DOMAIN 29.
... determined from Euler's relation, i8 e” = cosé + i sin 6. See Chiang (1984, Section 15.2) for a detailed exposition of the complex numbers. 11 An exponential signal x = Ae" may have real 2.3 FILTERS IN THE FREQUENCY DOMAIN 29.
Página 30
... determining the relative weight of each complex sinusoid. Similarly, Equation 2.9 is the analysis equation, which analyzes the original sequence xt to determine how much of each frequency component is required to synthesize it ...
... determining the relative weight of each complex sinusoid. Similarly, Equation 2.9 is the analysis equation, which analyzes the original sequence xt to determine how much of each frequency component is required to synthesize it ...
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