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 xii
... Output-Recurrent Model 8.4.2 Hidden-Recurrent Model 8.4.3 Output-Hidden Recurrent Model Network Selection 8.5. l Information Theoretic Criteria 8.5.2 Cross-Validation 8.5.3 Bayesian Regularization 8.5.4 Early Stopping 8.5.5 Bagging ...
... Output-Recurrent Model 8.4.2 Hidden-Recurrent Model 8.4.3 Output-Hidden Recurrent Model Network Selection 8.5. l Information Theoretic Criteria 8.5.2 Cross-Validation 8.5.3 Bayesian Regularization 8.5.4 Early Stopping 8.5.5 Bagging ...
Página xvi
... output-recurrent network A hidden-recurrent network An output-hidden-recurrent network Early stopping method Daily IBM log(price) predictions with a hidden-recurrent network model 237 240 245 246 250 256 257 257 261 264 277 277 278 278 ...
... output-recurrent network A hidden-recurrent network An output-hidden-recurrent network Early stopping method Daily IBM log(price) predictions with a hidden-recurrent network model 237 240 245 246 250 256 257 257 261 264 277 277 278 278 ...
Página 17
... output at time t. This may not be feasible in certain applications. As a result, some restrictions are imposed on a filter such that the output of the filter is not allowed to exist before the realization of the input—that is, CX) y =X ...
... output at time t. This may not be feasible in certain applications. As a result, some restrictions are imposed on a filter such that the output of the filter is not allowed to exist before the realization of the input—that is, CX) y =X ...
Página 18
... output. In other words, there is a feedback from the past filter outputs to the current filter output. A difference equation in this form can be solved by applying standard techniques." We will present only a simple example of linear ...
... output. In other words, there is a feedback from the past filter outputs to the current filter output. A difference equation in this form can be solved by applying standard techniques." We will present only a simple example of linear ...
Página 20
... output at time t, M data points at the beginning and N data points at the end of the output are missing. This feature makes a centered moving average less attractive in practice, in particular when the purpose is to forecast the future ...
... output at time t, M data points at the beginning and N data points at the end of the output are missing. This feature makes a centered moving average less attractive in practice, in particular when the purpose is to forecast the future ...
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