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 viii
... Filtering and the Kalman Filter 3.3.1 Recursive Mean Estimation 3.3.2. The Kalman Filter and Estimation Prediction with the Kalman Filter 3.4.1 Convergence of Kalman Gain 3.4.2 Example: Adaptive Expectations Vector Kalman Filter ...
... Filtering and the Kalman Filter 3.3.1 Recursive Mean Estimation 3.3.2. The Kalman Filter and Estimation Prediction with the Kalman Filter 3.4.1 Convergence of Kalman Gain 3.4.2 Example: Adaptive Expectations Vector Kalman Filter ...
Página xii
... Kalman Filter for Recurrent Networks 8.7.2 Multistream Training for Recurrent Networks 8.7.3 An Example: IBM Volatility Prediction Applications of Neural Network Models 8.8.1 Option Pricing 8.8.2 Filtering, Adaptation, and ...
... Kalman Filter for Recurrent Networks 8.7.2 Multistream Training for Recurrent Networks 8.7.3 An Example: IBM Volatility Prediction Applications of Neural Network Models 8.8.1 Option Pricing 8.8.2 Filtering, Adaptation, and ...
Página xiv
... Kalman filter estimate Simulated AR(2) process with a periodic component Simulated AR(2) process and its Kalman filterestimate Time varying beta estimation Example time series of sinusoids Partitioning of the time-frequency plane Sine ...
... Kalman filter estimate Simulated AR(2) process with a periodic component Simulated AR(2) process and its Kalman filterestimate Time varying beta estimation Example time series of sinusoids Partitioning of the time-frequency plane Sine ...
Página xvii
... filter coefficients Scalar Kalman filter simulation Scaling filter coefficients for selected Daubechies extremal phase wavelets Scaling filter coefficients for selected Daubechies least asymmetric wavelets Scaling filter coefficients ...
... filter coefficients Scalar Kalman filter simulation Scaling filter coefficients for selected Daubechies extremal phase wavelets Scaling filter coefficients for selected Daubechies least asymmetric wavelets Scaling filter coefficients ...
Página 13
... filtering as an optimum linearestimation problem. After reviewing the Wiener filter, the Kalman filter is introduced. The derivation of the Kalman filter is carried out in a single equation framework at several stages so that the reader ...
... filtering as an optimum linearestimation problem. After reviewing the Wiener filter, the Kalman filter is introduced. The derivation of the Kalman filter is carried out in a single equation framework at several stages so that the reader ...
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