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
... shift from a simple moving average Gain functions of a first-order difference 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 ...
... shift from a simple moving average Gain functions of a first-order difference 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 ...
Página 2
... shift. Since the STFT is simply applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to resolve events when they happen to fall within the width of the window. To ...
... shift. Since the STFT is simply applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to resolve events when they happen to fall within the width of the window. To ...
Página 7
... shift in variability while the high-level coefficients should be stationary. * If the structural break of interest is a possible change in the long-range dependence of the series, then all levels of wavelet coefficients should exhibit a ...
... shift in variability while the high-level coefficients should be stationary. * If the structural break of interest is a possible change in the long-range dependence of the series, then all levels of wavelet coefficients should exhibit a ...
Página 10
... shifts and transient shocks to a system. Figure 1.6 illustrates such a decomposition by separating the intraday variations of volatility from high-frequency data sampled at 20-min frequency and looking at the volatility at a scale of ...
... shifts and transient shocks to a system. Figure 1.6 illustrates such a decomposition by separating the intraday variations of volatility from high-frequency data sampled at 20-min frequency and looking at the volatility at a scale of ...
Página 27
... shift is ty2. These two curves are said to differ in phase (i.e., the location of the peak and the trough of the oscillation in each function is different in each period). 27 to any angle 6 would not change the value of sin 6 or cos 6 ...
... shift is ty2. These two curves are said to differ in phase (i.e., the location of the peak and the trough of the oscillation in each function is different in each period). 27 to any angle 6 would not change the value of sin 6 or cos 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