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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Dentro del libro
Página 9
... seventh scale corresponds to 0.89 day. Therefore, the seventh and higher scales are taken to be related with one-day and higher dynamics. - I.6 AGGREGATE HETEROGENEITY AND TIMESCALES Consider the participants of financial. 1.5. SCALING 9.
... seventh scale corresponds to 0.89 day. Therefore, the seventh and higher scales are taken to be related with one-day and higher dynamics. - I.6 AGGREGATE HETEROGENEITY AND TIMESCALES Consider the participants of financial. 1.5. SCALING 9.
Página 10
... dynamics from the global one, and the transitory from the permanent dynamics. Wavelet methods provide a natural platform to distinguish these effects from one another by decomposing a time series into different timescales. Furthermore ...
... dynamics from the global one, and the transitory from the permanent dynamics. Wavelet methods provide a natural platform to distinguish these effects from one another by decomposing a time series into different timescales. Furthermore ...
Página 34
... dynamics between 0 and f = 1/4. (c) A band-pass filter capturing the frequency dynamics between f = 1/8 and f = 1/4. (d) An all-pass filter without leaving out any frequency dynamics. 2.3.2 Low-Pass and High-Pass Filters The plot of the ...
... dynamics between 0 and f = 1/4. (c) A band-pass filter capturing the frequency dynamics between f = 1/8 and f = 1/4. (d) An all-pass filter without leaving out any frequency dynamics. 2.3.2 Low-Pass and High-Pass Filters The plot of the ...
Página 35
... dynamics between 0 and f = 1/4; an ideal band-pass filter capturing the frequency dynamics between f = 1/8 and f = 1/4; and an all-pass filter, which captures all of the frequency dynamics. An ideal filter is not computationally ...
... dynamics between 0 and f = 1/4; an ideal band-pass filter capturing the frequency dynamics between f = 1/8 and f = 1/4; and an all-pass filter, which captures all of the frequency dynamics. An ideal filter is not computationally ...
Página 41
... dynamics of the data-generating process. In financial markets, the data-generating process is a complex network of layers where each layer corresponds to a particular frequency. A successful characterization of such data-generating ...
... dynamics of the data-generating process. In financial markets, the data-generating process is a complex network of layers where each layer corresponds to a particular frequency. A successful characterization of such data-generating ...
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