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 4
... smooth of the AR(1) plus seasonal process (dotted line). s 4 ... / 27tt yi = 0.95y;-1 + 2. [. S1m ( P ) + 09. + €t (1.1) for t = 1,..., N, Pl = 3, P2 = 4, P = 5, and P1 = 6. The process has three, four, five, and six period stochastic ...
... smooth of the AR(1) plus seasonal process (dotted line). s 4 ... / 27tt yi = 0.95y;-1 + 2. [. S1m ( P ) + 09. + €t (1.1) for t = 1,..., N, Pl = 3, P2 = 4, P = 5, and P1 = 6. The process has three, four, five, and six period stochastic ...
Página 5
... smooth). Let us also assume that the noise process et is a sequence of uncorrelated Gaussian random variables with zero mean and variance o'. If we want the probability of any noise appearing in our estimate of y, to be as small as ...
... smooth). Let us also assume that the noise process et is a sequence of uncorrelated Gaussian random variables with zero mean and variance o'. If we want the probability of any noise appearing in our estimate of y, to be as small as ...
Página 11
... smooth corresponding to physical scale of approximately one-day December 1, 1986, through May 29, 1987. Data source: Olsen & Associates. Figure 1.7 shows the wavelet cross-correlation for the first six scales between the monthly DEM-USD ...
... smooth corresponding to physical scale of approximately one-day December 1, 1986, through May 29, 1987. Data source: Olsen & Associates. Figure 1.7 shows the wavelet cross-correlation for the first six scales between the monthly DEM-USD ...
Página 44
... smooth component. When X = 0, the smooth component is the data itself and no smoothing has occured. In the limit when X → co, the smooth component is a linear time trend. In many applications, X is typically set to X = 1600 for ...
... smooth component. When X = 0, the smooth component is the data itself and no smoothing has occured. In the limit when X → co, the smooth component is a linear time trend. In many applications, X is typically set to X = 1600 for ...
Página 45
... smooth that contains low-frequency components that extend approximately beyond 10 years. The cyclical component contains the business cycle dynamics (i.e., the variations with a period length of approximately 10 years or less). Several ...
... smooth that contains low-frequency components that extend approximately beyond 10 years. The cyclical component contains the business cycle dynamics (i.e., the variations with a period length of approximately 10 years or less). Several ...
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