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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... Rules Threshold Selection 6.3.1 Universal Thresholding 6.3.2 Minimax Estimation 6.3.3 Stein's Unbiased Risk Estimate 6.3.4. Hypothesis Testing 6.3.5 Bayesian Methodology 6.3.6 Cross-Validation Implementing Wavelet Denoising 6.4.1 ...
... Rules Threshold Selection 6.3.1 Universal Thresholding 6.3.2 Minimax Estimation 6.3.3 Stein's Unbiased Risk Estimate 6.3.4. Hypothesis Testing 6.3.5 Bayesian Methodology 6.3.6 Cross-Validation Implementing Wavelet Denoising 6.4.1 ...
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... rules Firm and nn-garrote thresholding rule The n-degree garrote thresholding rule Universal and minimax estimators Wavelet coefficients and SURE functions SURE and SURE hybrid thresholds Sample Brownian bridge process Brownian bridge ...
... rules Firm and nn-garrote thresholding rule The n-degree garrote thresholding rule Universal and minimax estimators Wavelet coefficients and SURE functions SURE and SURE hybrid thresholds Sample Brownian bridge process Brownian bridge ...
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... rules. The “universalestimator” corresponds to the V2O3 N threshold previously discussed, and the “minimax estimator” is discussed in Section 6.3.2. In each case a soft thresholding rule was utilized; essentially, all wavelet ...
... rules. The “universalestimator” corresponds to the V2O3 N threshold previously discussed, and the “minimax estimator” is discussed in Section 6.3.2. In each case a soft thresholding rule was utilized; essentially, all wavelet ...
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... rule are provided to facilitate easy implementation. The IBM stock prices, returns, and volatility are denoised using several combinations of thresholds and rules, along with an application to outlier testing. Chapter 7 looks at ...
... rule are provided to facilitate easy implementation. The IBM stock prices, returns, and volatility are denoised using several combinations of thresholds and rules, along with an application to outlier testing. Chapter 7 looks at ...
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... rule. The literature on nonlinearity in economics and finance is very rich. See, for example, Gallant (1987), Barnett (1989), Brock et al. (1991), Benhabib (1992), Härdle (1990), Dechert (1996), Bierens and Gallant (1997), Pagan and ...
... rule. The literature on nonlinearity in economics and finance is very rich. See, for example, Gallant (1987), Barnett (1989), Brock et al. (1991), Benhabib (1992), Härdle (1990), Dechert (1996), Bierens and Gallant (1997), Pagan and ...
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