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 xiii
... moving averages DEM-USD exchange rate and its simple moving averages Filter coefficients in a simple moving average A circle with radius R Cyclical functions Time series of representations of cyclical functions Fourier transform of a ...
... moving averages DEM-USD exchange rate and its simple moving averages Filter coefficients in a simple moving average A circle with radius R Cyclical functions Time series of representations of cyclical functions Fourier transform of a ...
Página 19
... 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-USD exchange. 1 ...
... 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-USD exchange. 1 ...
Página 20
... moving average filter outputs. (a) The original data. (b) One-day centered moving average with M = N = 144 (there are 288 5-min periods in a given day). (c) One-week centered moving average, M = N = 720 (there are 1440.5-min periods in ...
... moving average filter outputs. (a) The original data. (b) One-day centered moving average with M = N = 144 (there are 288 5-min periods in a given day). (c) One-week centered moving average, M = N = 720 (there are 1440.5-min periods in ...
Página 21
... normal curve. A similar technique that sometimes leads to confusion in the literature is called a Hamming filter ... moving average, which is used for smoothing mortality statistics, is another example of unequal filter coefficients ...
... normal curve. A similar technique that sometimes leads to confusion in the literature is called a Hamming filter ... moving average, which is used for smoothing mortality statistics, is another example of unequal filter coefficients ...
Página 22
... moving average is actually a convolution of four filters (Chatfield, 1984, page 43). w = (0.25, 0.25, 0.25, 0.25) + ... moving average yt (x1 + x;-1 + . . . .xt-M). | M + 1 One advantage of causal FIR filters is that there is no missing ...
... moving average is actually a convolution of four filters (Chatfield, 1984, page 43). w = (0.25, 0.25, 0.25, 0.25) + ... moving average yt (x1 + x;-1 + . . . .xt-M). | M + 1 One advantage of causal FIR filters is that there is no missing ...
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