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 19
... simple centered moving average, 1 * M - VII (xt-M +...+ x1–1 + x1 + xt-1 + . . . .xt-N), yt where all filter coefficients are equal. The impulse response of this filter is finite and flat: | .5 _------~~~~ - FIGURE 2, 1 Five-minute DEM ...
... simple centered moving average, 1 * M - VII (xt-M +...+ x1–1 + x1 + xt-1 + . . . .xt-N), yt where all filter coefficients are equal. The impulse response of this filter is finite and flat: | .5 _------~~~~ - FIGURE 2, 1 Five-minute DEM ...
Página 22
... simple moving average, it may be desirable to give more 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 ...
... simple moving average, it may be desirable to give more 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 ...
Página 23
... simple moving average filter outputs. (a) The original data. (b) Simple daily moving average (there are 288 5-min periods in one day so that M = 288). (c) Simple one-week moving average, M = 1440. (d) Simple two-week moving average, M ...
... simple moving average filter outputs. (a) The original data. (b) Simple daily moving average (there are 288 5-min periods in one day so that M = 288). (c) Simple one-week moving average, M = 1440. (d) Simple two-week moving average, M ...
Página 24
... simple moving average may increase and then decrease with increasing lags. A special case in economics is known as Almon lag (Almon, 1965). In an Almon lag specification, the filter coefficients may be determined according to the ...
... simple moving average may increase and then decrease with increasing lags. A special case in economics is known as Almon lag (Almon, 1965). In an Almon lag specification, the filter coefficients may be determined according to the ...
Página 25
... simple moving average as a function of the lag. (a) Constant coefficients: the coefficients are equal to each other at all lags. (b) Linear decay: the value of the coefficients decays linearly with increasing lags. (c) Geometric decay ...
... simple moving average as a function of the lag. (a) Constant coefficients: the coefficients are equal to each other at all lags. (b) Linear decay: the value of the coefficients decays linearly with increasing lags. (c) Geometric decay ...
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 |
Otras ediciones - Ver todas
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