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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... COVARIANCE ESTINATION Introduction The Wavelet Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence ... Covariance and Cross-Covariance 7.4. I Estimation 7.4.2 Confidence Intervals 7.4.3 Example: Monthly Foreign Exchange Rates ...
... COVARIANCE ESTINATION Introduction The Wavelet Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence ... Covariance and Cross-Covariance 7.4. I Estimation 7.4.2 Confidence Intervals 7.4.3 Example: Monthly Foreign Exchange Rates ...
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... Covariance matrices for an FDP(0.4) and its DWT Sample variances for the LA(8) coefficients of an FDP(0.45) Long memory estimation for IBM volatility Decomposition of x into DWPT coefficients Decomposition of x into DWT coefficients ...
... Covariance matrices for an FDP(0.4) and its DWT Sample variances for the LA(8) coefficients of an FDP(0.45) Long memory estimation for IBM volatility Decomposition of x into DWPT coefficients Decomposition of x into DWT coefficients ...
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... covariance between the DEM-USD and JPY-USD exchange rate returns Wavelet cross-correlation between the DEM-USD and JPY-USD exchange rate returns Wavelet variance for 20-min absolute returns Threshold activation function Logistic ...
... covariance between the DEM-USD and JPY-USD exchange rate returns Wavelet cross-correlation between the DEM-USD and JPY-USD exchange rate returns Wavelet variance for 20-min absolute returns Threshold activation function Logistic ...
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... covariance of the process do not vary over time. For quite a few observed time series, this assumption is suspect and statistical hypothesis testing is useful in detecting and locating deviations from stationarity at specific points ...
... covariance of the process do not vary over time. For quite a few observed time series, this assumption is suspect and statistical hypothesis testing is useful in detecting and locating deviations from stationarity at specific points ...
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... covariance and cross-correlation) may be 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 ...
... covariance and cross-correlation) may be 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 ...
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