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
... 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 Prices 4.5.5 Example: AR(1) with ...
... 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 Prices 4.5.5 Example: AR(1) with ...
Página xi
... Returns 6.5.3 IBM Volatility 6.5.4 Outlier Testing 7 VVAVELETS FORVARIANCE-COVARIANCE ESTINATION Introduction The Wavelet Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence Intervals for the Wavelet Variance 7.2.3 Example ...
... Returns 6.5.3 IBM Volatility 6.5.4 Outlier Testing 7 VVAVELETS FORVARIANCE-COVARIANCE ESTINATION Introduction The Wavelet Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence Intervals for the Wavelet Variance 7.2.3 Example ...
Página xiv
... returns DWT multiresolution analysis of the IBM volatility series Phase diagram for the OLG pricing function ... returns MODWT multiresolution analysis of the IBM volatility Sample ACF for a simulated AR(1) process Sample ACF for the 5 ...
... returns DWT multiresolution analysis of the IBM volatility series Phase diagram for the OLG pricing function ... returns MODWT multiresolution analysis of the IBM volatility Sample ACF for a simulated AR(1) process Sample ACF for the 5 ...
Página xv
... returns and wavelet denoising estimates IBM volatility and wavelet denoising estimates Wavelet-based outlier detection 157 160 164 166 168 171 175 177 178 179 181 182 184 186 187 190 195 197 198 199 7. I 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9.
... returns and wavelet denoising estimates IBM volatility and wavelet denoising estimates Wavelet-based outlier detection 157 160 164 166 168 171 175 177 178 179 181 182 184 186 187 190 195 197 198 199 7. I 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9.
Página xvi
... returns Wavelet variance for the DEM-USD and JPY-USD exchange rate returns Wavelet 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 ...
... returns Wavelet variance for the DEM-USD and JPY-USD exchange rate returns Wavelet 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 ...
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