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 22
... weight to more recent inputs and less weight to inputs corresponding to the distant past. For example, Fischer (1937) proposed the following linearly declining weights in a simple moving average: w6 = M + 1 wi = M w; = M – 1 wM = 1 ...
... weight to more recent inputs and less weight to inputs corresponding to the distant past. For example, Fischer (1937) proposed the following linearly declining weights in a simple moving average: w6 = M + 1 wi = M w; = M – 1 wM = 1 ...
Página 23
... weights in a simple moving average decline exponentially at distant lags, the resulting filter is known as the exponentially weighted moving average (EWMA). For example, if wi = p", where p is an arbitrary constant between 0 and 1, and ...
... weights in a simple moving average decline exponentially at distant lags, the resulting filter is known as the exponentially weighted moving average (EWMA). For example, if wi = p", where p is an arbitrary constant between 0 and 1, and ...
Página 24
... weights obey, f 0 u)0 - q) f 1 w1 - q) f 2 wu = p", which is M M 1 f y = <=7 X wixt-i = X tDi Xt—i, XD w; i=0 l i=0 where wi = w/X w. For p = 0.40 and M = 5, the moving average is 1 * = 1.66 (s +0.40x_1+0.40°x_2 +0.40'x'_3+ 0.40'x'_4 + ...
... weights obey, f 0 u)0 - q) f 1 w1 - q) f 2 wu = p", which is M M 1 f y = <=7 X wixt-i = X tDi Xt—i, XD w; i=0 l i=0 where wi = w/X w. For p = 0.40 and M = 5, the moving average is 1 * = 1.66 (s +0.40x_1+0.40°x_2 +0.40'x'_3+ 0.40'x'_4 + ...
Página 30
... 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 (Bloomfield, 2000). Given x, and X ...
... 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 (Bloomfield, 2000). Given x, and X ...
Página 31
... weights of these periodic components are much higher than any other component of the signal. 2.3.1 Frequency Response Impulse response function in the time domain is a useful tool for describing and classifying linear filters. An ...
... weights of these periodic components are much higher than any other component of the signal. 2.3.1 Frequency Response Impulse response function in the time domain is a useful tool for describing and classifying linear filters. An ...
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