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
... Prediction with the Kalman Filter 3.4.1 Convergence of Kalman Gain 3.4.2 Example: Adaptive Expectations Vector Kalman Filter Estimation 3.5.1 Time-Varying Coefficients in a Regression 3.5.2 An Autoregressive Model 3.5.3 A Simple Vector ...
... Prediction with the Kalman Filter 3.4.1 Convergence of Kalman Gain 3.4.2 Example: Adaptive Expectations Vector Kalman Filter Estimation 3.5.1 Time-Varying Coefficients in a Regression 3.5.2 An Autoregressive Model 3.5.3 A Simple Vector ...
Página xii
... Prediction 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 ...
... Prediction 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 ...
Página xvi
... predictions with a single-layer feedforward network model Daily IBM volatility predictions with a single-layer feedforward network model Daily IBM volatility predictions with a kernel regression Typical feedback in a recurrent network ...
... predictions with a single-layer feedforward network model Daily IBM volatility predictions with a single-layer feedforward network model Daily IBM volatility predictions with a kernel regression Typical feedback in a recurrent network ...
Página xvii
... Summary statistics for the daily exchange rates Out-of-sample predictions with technical indicators 46 82 114 115 119 152 153 159 167 215 249 265 308 309 312 3.14 This Page Intentionally Left Blank ACKNOWLEDGN ENTS e are thankful xvii.
... Summary statistics for the daily exchange rates Out-of-sample predictions with technical indicators 46 82 114 115 119 152 153 159 167 215 249 265 308 309 312 3.14 This Page Intentionally Left Blank ACKNOWLEDGN ENTS e are thankful xvii.
Página 14
... network applications in financial markets, such as volatility prediction, predictibility inforeign exchange markets, and option pricing. LINEAR FILTERS 2. INTRODUCTION The inherent interaction of a particular |4 CHAPTER 1 INTRODUCTION.
... network applications in financial markets, such as volatility prediction, predictibility inforeign exchange markets, and option pricing. LINEAR FILTERS 2. INTRODUCTION The inherent interaction of a particular |4 CHAPTER 1 INTRODUCTION.
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