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
... equation Volatility of DEM-USD and VaR estimates (1.650) Gain functions of an EWMA and a simple moving average Gain functions of the Hodrick-Prescott and an ideal filter Filtered log quarterly U.S. real gross national product 2. 16 3. I ...
... equation Volatility of DEM-USD and VaR estimates (1.650) Gain functions of an EWMA and a simple moving average Gain functions of the Hodrick-Prescott and an ideal filter Filtered log quarterly U.S. real gross national product 2. 16 3. I ...
Página 18
... 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, yt = ayt–1 ...
... 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, yt = ayt–1 ...
Página 19
... Equation 2.6. Given the unit impulse input in Equation 2.4, the impulse response of the filter is yO = x0 = 1 y1 = ay0 + 0 = a y2 = ay, 4-0 = a” y = a'. The filter has an impulse response with infinite duration. Therefore, it is an ...
... Equation 2.6. Given the unit impulse input in Equation 2.4, the impulse response of the filter is yO = x0 = 1 y1 = ay0 + 0 = a y2 = ay, 4-0 = a” y = a'. The filter has an impulse response with infinite duration. Therefore, it is an ...
Página 28
... Equation 2.7. The successive values of x are (–1, 1, –1, 1, • * * * –1, 1), so that there are six oscillations (N/p = 12/2) and each oscillation (+1) completes itself in two time periods. The number of cycles per unit time is 1/2 ...
... Equation 2.7. The successive values of x are (–1, 1, –1, 1, • * * * –1, 1), so that there are six oscillations (N/p = 12/2) and each oscillation (+1) completes itself in two time periods. The number of cycles per unit time is 1/2 ...
Página 29
... Equation 2.8, X(f) is given by X(f) = XD xe". (2.9) t=-oo Equation 2.8 is referred as the inverse Fourier transform, and Equation 2.9 is the Fourier transform of xt. Two equations constitute a Fourier representation of the FIGURE 2.6 ...
... Equation 2.8, X(f) is given by X(f) = XD xe". (2.9) t=-oo Equation 2.8 is referred as the inverse Fourier transform, and Equation 2.9 is the Fourier transform of xt. Two equations constitute a Fourier representation of the FIGURE 2.6 ...
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