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 10
... produces an alternative, known as the wavelet variance, to the periodogram. This variance decomposition may be ... producing the wavelet cross-covariance and wavelet cross-correlation. The waveletcross-covariance decomposes the cross ...
... produces an alternative, known as the wavelet variance, to the periodogram. This variance decomposition may be ... producing the wavelet cross-covariance and wavelet cross-correlation. The waveletcross-covariance decomposes the cross ...
Página 15
... produce complicated features. Filtering methods deal with the identification and extraction of certain features (e.g., trends, seasonalities) from a time series, which are important in terms of modeling and inference. Filtering is a ...
... produce complicated features. Filtering methods deal with the identification and extraction of certain features (e.g., trends, seasonalities) from a time series, which are important in terms of modeling and inference. Filtering is a ...
Página 45
... produces a smooth that contains low-frequency components that extend approximately beyond 10 years. The cyclical component contains the business cycle dynamics (i.e., the variations with a period length of approximately 10 years or less) ...
... produces a smooth that contains low-frequency components that extend approximately beyond 10 years. The cyclical component contains the business cycle dynamics (i.e., the variations with a period length of approximately 10 years or less) ...
Página 48
... producing buy and sell signals in a foreign exchange market. A chartist may place a confidence interval (a band) ... produced. Instead of ad hoc rules to create aband around the simple moving averages, a statistical measure may also be ...
... producing buy and sell signals in a foreign exchange market. A chartist may place a confidence interval (a band) ... produced. Instead of ad hoc rules to create aband around the simple moving averages, a statistical measure may also be ...
Página 99
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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