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 28
... observation period, one peak and one trough, and the number of cycles per unit time is 1/N. However, if p is different than N, the number of cycles per unit time is 1/p." To illustrate, suppose that N = 12, a = 1.57, and p = 2 in ...
... observation period, one peak and one trough, and the number of cycles per unit time is 1/N. However, if p is different than N, the number of cycles per unit time is 1/p." To illustrate, suppose that N = 12, a = 1.57, and p = 2 in ...
Página 41
... observations. For large averaging periods, the following approximation to Equation 2.27 might be utilized: N-1 6 = |(1-x) XXI (r.-, - F)* (2.28) i =0 since XX, X = 1/(1-x) as N - Co. The RiskMetrics technical document recommends the ...
... observations. For large averaging periods, the following approximation to Equation 2.27 might be utilized: N-1 6 = |(1-x) XXI (r.-, - F)* (2.28) i =0 since XX, X = 1/(1-x) as N - Co. The RiskMetrics technical document recommends the ...
Página 43
... observation. Equation 2.32 may also be modified as 6 = x(r. — F)* + (1–3)(6 1 + st-1), (2.33) where st-1 is obtained from the following equation: s = y(6 – 6: 1) + (1 – y)st-1, (2.34) and 0 < y < 1. Equation 2.33 and Equation 2.34 are ...
... observation. Equation 2.32 may also be modified as 6 = x(r. — F)* + (1–3)(6 1 + st-1), (2.33) where st-1 is obtained from the following equation: s = y(6 – 6: 1) + (1 – y)st-1, (2.34) and 0 < y < 1. Equation 2.33 and Equation 2.34 are ...
Página 51
... observations, yf = x t + ét, (3.1) where y is the observation, et is the noise, and x is the desired signal from the realization of a random process. In this context, filtering involves the estimation of signal x1 in Equation 3.1. In ...
... observations, yf = x t + ét, (3.1) where y is the observation, et is the noise, and x is the desired signal from the realization of a random process. In this context, filtering involves the estimation of signal x1 in Equation 3.1. In ...
Página 52
... observation yi. Given the observation set,” y = (y1, y2, y3, ... , yN), a linear estimate of x at time N may be obtained by assigning a weight wi to each observation and calculating a weighted average of the observations via A. 3: N = w ...
... observation yi. Given the observation set,” y = (y1, y2, y3, ... , yN), a linear estimate of x at time N may be obtained by assigning a weight wi to each observation and calculating a weighted average of the observations via A. 3: N = w ...
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