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 7
... decomposition of the wavelet transform allows for a straightforward testing procedure to be applied to each level of the transform instead of developing customized procedures to deal with each type of structural break. Figure 1.4 shows ...
... decomposition of the wavelet transform allows for a straightforward testing procedure to be applied to each level of the transform instead of developing customized procedures to deal with each type of structural break. Figure 1.4 shows ...
Página 8
... decomposition. The top row is the original IBM volatility series, and the following three rows are the normalized cumulative sum of squares (NCSS) of the first three levels of its wavelet decomposition. These three levels are associate ...
... decomposition. The top row is the original IBM volatility series, and the following three rows are the normalized cumulative sum of squares (NCSS) of the first three levels of its wavelet decomposition. These three levels are associate ...
Página 9
... decomposition of the variance on a scale-by-scale basis through a wavelet multiresolution analysis. For example, the first wavelet scale is associated with changes at 20-min, the second wavelet scale is associated with 40-min changes ...
... decomposition of the variance on a scale-by-scale basis through a wavelet multiresolution analysis. For example, the first wavelet scale is associated with changes at 20-min, the second wavelet scale is associated with 40-min changes ...
Página 10
... 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 one day. Here, it is evident that the volatility burst during the first ...
... 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 one day. Here, it is evident that the volatility burst during the first ...
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... the lead-lag relation in foreign exchange markets, would have remained hidden without the multiscale decomposition of the wavelet transform. I-I"E- D.5 U.D. - - - - - -0.5 Level. 1.7. MULTISCALE CROSS-CORRELATION | |
... the lead-lag relation in foreign exchange markets, would have remained hidden without the multiscale decomposition of the wavelet transform. I-I"E- D.5 U.D. - - - - - -0.5 Level. 1.7. MULTISCALE CROSS-CORRELATION | |
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