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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... Recurrent Networks 8.4.1 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 ...
... Recurrent Networks 8.4.1 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 ...
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... recurrent network An 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 ...
... recurrent network An 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 ...
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... recurrent networks) in addition to simple feedforward networks. Recurrent networks together with multistream learning provide a xxi PREFACE.
... recurrent networks) in addition to simple feedforward networks. Recurrent networks together with multistream learning provide a xxi PREFACE.
Página xxii
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. feedforward networks. Recurrent networks together with multistream learning provide a rich filtering framework for long-memory processes. This book contains numerous empirical ...
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. feedforward networks. Recurrent networks together with multistream learning provide a rich filtering framework for long-memory processes. This book contains numerous empirical ...
Página 14
... recurrent neural network models. The chapter also provides some examples of neural network applications in financial markets, such as volatility prediction, predictibility inforeign exchange markets, and option pricing. LINEAR FILTERS 2 ...
... recurrent neural network models. The chapter also provides some examples of neural network applications in financial markets, such as volatility prediction, predictibility inforeign exchange markets, and option pricing. LINEAR FILTERS 2 ...
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 |
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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