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
... Networks 8.3.1 Example: IBM Stock Price and Volatility 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 ...
... Networks 8.3.1 Example: IBM Stock Price and Volatility 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 ...
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... network A single-layer feedforward network A two-layer feedforward network Daily IBM stock prices Daily IBM log(price) predictions with a single-layer feedforward network model Daily IBM volatility predictions with a single-layer ...
... network A single-layer feedforward network A two-layer feedforward network Daily IBM stock prices Daily IBM log(price) predictions with a single-layer feedforward network model Daily IBM volatility predictions with a single-layer ...
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... network filters. The focus in this chapter is confined to the function approximation and nonlinear filtering capabilities of neural network models. It starts with the elements of a typical neural network model and progresses through ...
... network filters. The focus in this chapter is confined to the function approximation and nonlinear filtering capabilities of neural network models. It starts with the elements of a typical neural network model and progresses through ...
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... models are studied in Gençay et al. (2001c, 2002), where the authors study the performance of a widely used commercial real-time trading model ... network of layers where each layer corresponds to a particular frequency. A successful ...
... models are studied in Gençay et al. (2001c, 2002), where the authors study the performance of a widely used commercial real-time trading model ... network of layers where each layer corresponds to a particular frequency. A successful ...
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... models. Also, an optimization of technical trading strategies with neural network models may result in some profitability in security markets; see, for example, Gençay and Stengos (1998) and Gençay (1998a, 1998b; 1999). The most ...
... models. Also, an optimization of technical trading strategies with neural network models may result in some profitability in security markets; see, for example, Gençay and Stengos (1998) and Gençay (1998a, 1998b; 1999). The most ...
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