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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Dentro del libro
Resultados 1-5 de 82
Página ix
... Variance 4.4.5 Example: IBM Returns 4.4.6 Example: An Overlapping Generations Model The Maximal Overlap Discrete Wavelet Transform 4.5. I Definition 4.5.2 Multiresolution Analysis 4.5.3 Analysis of Variance 4.5.4 Example: IBM Stock ...
... Variance 4.4.5 Example: IBM Returns 4.4.6 Example: An Overlapping Generations Model The Maximal Overlap Discrete Wavelet Transform 4.5. I Definition 4.5.2 Multiresolution Analysis 4.5.3 Analysis of Variance 4.5.4 Example: IBM Stock ...
Página xi
... Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence Intervals for the Wavelet Variance 7.2.3 Example: Simulated AR(1) Model 7.2.4 Example: IBM Stock Prices Testing Homogeneity of Variance 7.3. I Locating a Variance Change ...
... Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence Intervals for the Wavelet Variance 7.2.3 Example: Simulated AR(1) Model 7.2.4 Example: IBM Stock Prices Testing Homogeneity of Variance 7.3. I Locating a Variance Change ...
Página xvi
... variance Wavelet variances for an AR(1) process Wavelet variance for the IBM stock price volatility Locating a change in IBM stock price volatility Monthly foreign exchange returns Wavelet variance for the DEM-USD and JPY-USD exchange ...
... variance Wavelet variances for an AR(1) process Wavelet variance for the IBM stock price volatility Locating a change in IBM stock price volatility Monthly foreign exchange returns Wavelet variance for the DEM-USD and JPY-USD exchange ...
Página 7
... variance, then the low-level wavelet coefficients (which are associated with high-frequency content of the time series) should retain this sudden shift in variability while the high-level coefficients should be stationary. * If the ...
... variance, then the low-level wavelet coefficients (which are associated with high-frequency content of the time series) should retain this sudden shift in variability while the high-level coefficients should be stationary. * If the ...
Página 9
... variance for 20-min absolute returns of (a) DEM-USD and (c) JPY-USD. In (a) and (c), the estimated wavelet variances are plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the estimated variances for each ...
... variance for 20-min absolute returns of (a) DEM-USD and (c) JPY-USD. In (a) and (c), the estimated wavelet variances are plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the estimated variances for each ...
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