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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Dentro del libro
Página 10
... given trading day but do not carry overnight positions. Then there are day traders, who may carry positions overnight, short-term traders and long-term traders. Each of these classes of traders may have its own trading tool sets ...
... given trading day but do not carry overnight positions. Then there are day traders, who may carry positions overnight, short-term traders and long-term traders. Each of these classes of traders may have its own trading tool sets ...
Página 11
... given so that significant cross-correlations may be easily identified. As the wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be expected since ...
... given so that significant cross-correlations may be easily identified. As the wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be expected since ...
Página 17
... Given a unit impulse signal, the output sequence of a filter is known as the impulse response of that filter. If the impulse response of a filter is finite, it is called a finite impulse response filter, or an FIR filter. On the other ...
... Given a unit impulse signal, the output sequence of a filter is known as the impulse response of that filter. If the impulse response of a filter is finite, it is called a finite impulse response filter, or an FIR filter. On the other ...
Página 20
... given day). (c) One-week centered moving average, M = N = 720 (there are 1440.5-min periods in a given business week). (d) Two weeks centered moving average, M = N = 1440. Note that M data points at the beginning and N data points at ...
... given day). (c) One-week centered moving average, M = N = 720 (there are 1440.5-min periods in a given business week). (d) Two weeks centered moving average, M = N = 1440. Note that M data points at the beginning and N data points at ...
Página 21
... given the original input x1, the first step is to filter the input by using the first set of filter coefficients w1: M Zf E. XD. 101,i Xt—i. i=-N At the second stage, the first stage filter output z is filtered once again by using the ...
... given the original input x1, the first step is to filter the input by using the first set of filter coefficients w1: M Zf E. XD. 101,i Xt—i. i=-N At the second stage, the first stage filter output z is filtered once again by using the ...
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