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 18
... difference equation in this form can be solved by applying standard techniques." We will present only a simple example of linear difference equations to relate them with filtering. Consider the following first-order difference equation ...
... difference equation in this form can be solved by applying standard techniques." We will present only a simple example of linear difference equations to relate them with filtering. Consider the following first-order difference equation ...
Página 19
... difference equation would have an impulse response with infinite duration. If the difference equation is stable as a system, the impulse response will decay asymptotically toward zero. On the other hand, if the system is not stable, the ...
... difference equation would have an impulse response with infinite duration. If the difference equation is stable as a system, the impulse response will decay asymptotically toward zero. On the other hand, if the system is not stable, the ...
Página 38
... difference yt = 0.50xt - 0.50xt_1 has the following frequency response function: hop. –. #(1–e"). = #(e." - ..") e-itf = sin (t,f) ie", where the gain is zero at zero frequency (f = 0) and increases with increasing f, reaching its maximum ...
... difference yt = 0.50xt - 0.50xt_1 has the following frequency response function: hop. –. #(1–e"). = #(e." - ..") e-itf = sin (t,f) ie", where the gain is zero at zero frequency (f = 0) and increases with increasing f, reaching its maximum ...
Página 39
... difference equation in Equation 2.25 with different parameter settings. The sign of the parameter a determines whether the linear difference equation is a low-pass filter as in the left panel or a high-pass filter as in the right panel ...
... difference equation in Equation 2.25 with different parameter settings. The sign of the parameter a determines whether the linear difference equation is a low-pass filter as in the left panel or a high-pass filter as in the right panel ...
Página 41
... difference (return), and r is period average and X is the decay factor (0 < x < 1). The decay factor determines the relative importance of recent observations. For large averaging periods, the following approximation to Equation 2.27 ...
... difference (return), and r is period average and X is the decay factor (0 < x < 1). The decay factor determines the relative importance of recent observations. For large averaging periods, the following approximation to Equation 2.27 ...
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