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 2
... studied in Chapter 5. I. I. FOURIERVERSUS WAVELET ANALYSIS At this point, a natural question to ask would be why not use traditional spectral tools such as the Fourier analysis rather than exploring wavelet methods? Fourier series is a ...
... studied in Chapter 5. I. I. FOURIERVERSUS WAVELET ANALYSIS At this point, a natural question to ask would be why not use traditional spectral tools such as the Fourier analysis rather than exploring wavelet methods? Fourier series is a ...
Página 3
... studying nonstationary or transient time series. The following examples demonstrate the convenient usage of wavelet-based methods in seasonality filtering, denoising, identification of structural breaks, scaling, separating observed ...
... studying nonstationary or transient time series. The following examples demonstrate the convenient usage of wavelet-based methods in seasonality filtering, denoising, identification of structural breaks, scaling, separating observed ...
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... different data frequencies, we proceed with a multiscale approach. The studied data sets are the 20-min Deutsche Mark – U.S. Dollar (DEM-USD) and Japanese Yen – U.S. Dollar | Data go © – <t o - o * 1.5. SCALING 7 1.5 Scaling.
... different data frequencies, we proceed with a multiscale approach. The studied data sets are the 20-min Deutsche Mark – U.S. Dollar (DEM-USD) and Japanese Yen – U.S. Dollar | Data go © – <t o - o * 1.5. SCALING 7 1.5 Scaling.
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... studied forecasting financial and economic cycles in general, and Weigend and Gerschenfeld (1994) investigated time series prediction in natural and physical phenomeana. Kaiser and Maraval (2000) and Diebold and Rudebusch (1999) ...
... studied forecasting financial and economic cycles in general, and Weigend and Gerschenfeld (1994) investigated time series prediction in natural and physical phenomeana. Kaiser and Maraval (2000) and Diebold and Rudebusch (1999) ...
Página 41
... studied in Gençay et al. (2001c, 2002), where the authors study the performance of a widely used commercial real-time trading model and compare it with the performance of systematic currency traders. Gençay et al. (2001c) argued that it ...
... studied in Gençay et al. (2001c, 2002), where the authors study the performance of a widely used commercial real-time trading model and compare it with the performance of systematic currency traders. Gençay et al. (2001c) argued that it ...
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