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 89
Página xiii
... Wavelet cross-correlation between exchange rate returns DEM-USD exchange rate and its centered moving averages DEM-USD exchange rate and its simple moving averages Filter coefficients in a simple moving average A circle with radius R ...
... Wavelet cross-correlation between exchange rate returns DEM-USD exchange rate and its centered moving averages DEM-USD exchange rate and its simple moving averages Filter coefficients in a simple moving average A circle with radius R ...
Página xiv
... wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet approximations Haar wavelet filter coefficients The Haar wavelet ...
... wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet approximations Haar wavelet filter coefficients The Haar wavelet ...
Página xvii
... filter coefficients Scalar Kalman filter simulation Scaling filter coefficients for selected Daubechies extremal ... wavelet coefficients Minimax thresholds Results of testing IBM stock volatility for homogeneity of variance Wavelet ...
... filter coefficients Scalar Kalman filter simulation Scaling filter coefficients for selected Daubechies extremal ... wavelet coefficients Minimax thresholds Results of testing IBM stock volatility for homogeneity of variance Wavelet ...
Página xxii
... filter techniques but also provide guidance for potential application areas. Some of the finance applications are presented with high-frequency financial time series. This provides a platform for the usefulness of the wavelet methods in ...
... filter techniques but also provide guidance for potential application areas. Some of the finance applications are presented with high-frequency financial time series. This provides a platform for the usefulness of the wavelet methods in ...
Página 2
... WAVELET ANALYSIS At this point, a natural question to ask would be why not use traditional spectral tools such as ... filter, and a shifted version of the same function backward in time (Figure 1.1b). The wavelet filter is long in time ...
... WAVELET ANALYSIS At this point, a natural question to ask would be why not use traditional spectral tools such as ... filter, and a shifted version of the same function backward in time (Figure 1.1b). The wavelet filter is long in time ...
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