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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... Intervals for the Wavelet Variance 7.2.3 Example: Simulated AR(1) Model 7.2.4 Example: IBM Stock Prices Testing ... Intervals 7.4.3 Example: Monthly Foreign Exchange Rates The Wavelet Correlation and Cross-Correlation 7.5.1 Estimation ...
... Intervals for the Wavelet Variance 7.2.3 Example: Simulated AR(1) Model 7.2.4 Example: IBM Stock Prices Testing ... Intervals 7.4.3 Example: Monthly Foreign Exchange Rates The Wavelet Correlation and Cross-Correlation 7.5.1 Estimation ...
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... intervals of about an hour. The goal is to 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 ...
... intervals of about an hour. The goal is to 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 ...
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... intervals are given so that significant cross-correlations may be easily identified. As the wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be ...
... intervals are given so that significant cross-correlations may be easily identified. As the wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be ...
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... interval for the wavelet cross-correlation. At each wavelet scale, there are several lags very close to zero where the confidence interval for the wavelet cross-correlation does not include zero and therefore indicates significant ...
... interval for the wavelet cross-correlation. At each wavelet scale, there are several lags very close to zero where the confidence interval for the wavelet cross-correlation does not include zero and therefore indicates significant ...
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... intervals. The wavelet variance decomposes the variance of a time series on a scale-by-scale basis and is related to the spectrum. An application for testing for a change in volatility, using the IBM time series, is presented. The ...
... intervals. The wavelet variance decomposes the variance of a time series on a scale-by-scale basis and is related to the spectrum. An application for testing for a change in volatility, using the IBM time series, is presented. The ...
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