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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... Standard Denoising 6.4.2 Translation-Invariant Denoising Applications 167 170 172 174 176 176 179 180 183 183 185 188 189 192 194 194 196 198 202 207 209 209 210 212 213 213 214 218 221 222 224 224 227 227 7. I 7.2 7.3 7.4 7.5 7.6 7.7 ...
... Standard Denoising 6.4.2 Translation-Invariant Denoising Applications 167 170 172 174 176 176 179 180 183 183 185 188 189 192 194 194 196 198 202 207 209 209 210 212 213 213 214 218 221 222 224 224 227 227 7. I 7.2 7.3 7.4 7.5 7.6 7.7 ...
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... standard signal plus noise model; that is, yi = St + ét, t = 0, 1, . . . , N – 1. (1.2) For now letus assumes, is a deterministic function of t and associated with lower frequency oscillations (i.e., it is relatively smooth). Let us ...
... standard signal plus noise model; that is, yi = St + ét, t = 0, 1, . . . , N – 1. (1.2) For now letus assumes, is a deterministic function of t and associated with lower frequency oscillations (i.e., it is relatively smooth). Let us ...
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... Standard time-domain measures of association for multivariate time series (e.g., cross-covariance and cross-correlation) may be defined using the coefficients from the application of the wavelet transform to each series, thus producing ...
... Standard time-domain measures of association for multivariate time series (e.g., cross-covariance and cross-correlation) may be defined using the coefficients from the application of the wavelet transform to each series, thus producing ...
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... Standard methods for deriving the threshold and selecting a thresholding rule are provided to facilitate easy implementation. The IBM stock prices, returns, and volatility are denoised using several combinations of thresholds and rules ...
... Standard methods for deriving the threshold and selecting a thresholding rule are provided to facilitate easy implementation. The IBM stock prices, returns, and volatility are denoised using several combinations of thresholds and rules ...
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... standard references in signal processing and filtering are Anderson and Moore (1979), Oppenheim and Schafer (1989), and, more recently, Strang and Nguyen (1996) and Mallat (1998). There are several books in time series analysis of ...
... standard references in signal processing and filtering are Anderson and Moore (1979), Oppenheim and Schafer (1989), and, more recently, Strang and Nguyen (1996) and Mallat (1998). There are several books in time series analysis of ...
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