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 42
... dynamics than a simple moving average filter output, and hence it provides a better local estimate of volatility than a simple moving average. An estimate of the daily standard deviation may also be obtained with an infinite lag ...
... dynamics than a simple moving average filter output, and hence it provides a better local estimate of volatility than a simple moving average. An estimate of the daily standard deviation may also be obtained with an infinite lag ...
Página 43
... dynamics than a simple moving average filter output and hence it provides a better local estimate of volatility. which might be written as 6 = x(r. — r) + X(1-X)(r_1 - F)* + X(1–X)*(r_2 –F)* + . . . . (2.30) If we shift Equation 2.30 ...
... dynamics than a simple moving average filter output and hence it provides a better local estimate of volatility. which might be written as 6 = x(r. — r) + X(1-X)(r_1 - F)* + X(1–X)*(r_2 –F)* + . . . . (2.30) If we shift Equation 2.30 ...
Página 44
... dynamics as the magnitude of the frequency response is zero when f = 0. The cyclical component starts picking up the frequency components of the original time series at around f = 0.025. Notice that f = 0.025 corresponds to a period ...
... dynamics as the magnitude of the frequency response is zero when f = 0. The cyclical component starts picking up the frequency components of the original time series at around f = 0.025. Notice that f = 0.025 corresponds to a period ...
Página 45
... dynamics (i.e., the variations with a period length of approximately 10 years or less). Several studies criticize the HP filter on the ground that it distorts the dynamics of the original time series; see, for example, Cogley and Nason ...
... dynamics (i.e., the variations with a period length of approximately 10 years or less). Several studies criticize the HP filter on the ground that it distorts the dynamics of the original time series; see, for example, Cogley and Nason ...
Página 47
... dynamic properties and that its cyclical component fails to capture a significant fraction of the variability in business-cycle frequencies (Guay and St-Amant, 1997; Murray, 2001). Nevertheless, both the HP and BK filters have become ...
... dynamic properties and that its cyclical component fails to capture a significant fraction of the variability in business-cycle frequencies (Guay and St-Amant, 1997; Murray, 2001). Nevertheless, both the HP and BK filters have become ...
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