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 ix
... Definition 4.5.2 Multiresolution Analysis 4.5.3 Analysis of Variance 4.5.4 Example: IBM Stock Prices 4.5.5 Example: AR(1) with Seasonalities Practical Issues in Implementation 4.6.1 Selecting a Wavelet Basis 4.6.2. Nondyadic Length Time ...
... Definition 4.5.2 Multiresolution Analysis 4.5.3 Analysis of Variance 4.5.4 Example: IBM Stock Prices 4.5.5 Example: AR(1) with Seasonalities Practical Issues in Implementation 4.6.1 Selecting a Wavelet Basis 4.6.2. Nondyadic Length Time ...
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... definition of a stationary time series). However, restricting ourselves to stationary time series is not very appealing since most economic/financial time series exhibit quite complicated patterns over time (e.g., trends, abrupt changes ...
... definition of a stationary time series). However, restricting ourselves to stationary time series is not very appealing since most economic/financial time series exhibit quite complicated patterns over time (e.g., trends, abrupt changes ...
Página 7
... define a superior realized volatility, which combines the low noise of short return interval sizes with the low bias of large return intervals. Instead of calculating realized volatilities at different data frequencies, we proceed with ...
... define a superior realized volatility, which combines the low noise of short return interval sizes with the low bias of large return intervals. Instead of calculating realized volatilities at different data frequencies, we proceed with ...
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... defined as the absolute value of the returns. Our results provide evidence that the scaling behavior of volatility breaks at scales higher than one day. Figure 1.5 reports the decomposition of the variance on a scale-by-scale basis ...
... defined as the absolute value of the returns. Our results provide evidence that the scaling behavior of volatility breaks at scales higher than one day. Figure 1.5 reports the decomposition of the variance on a scale-by-scale basis ...
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... defined using the coefficients from the application of the wavelet transform to each series, thus producing the wavelet cross-covariance and wavelet cross-correlation. The waveletcross-covariance decomposes the cross-covariance between ...
... defined using the coefficients from the application of the wavelet transform to each series, thus producing the wavelet cross-covariance and wavelet cross-correlation. The waveletcross-covariance decomposes the cross-covariance between ...
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