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
... 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 linear combination of sines and cosines. Each of these sines and ...
... 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 linear combination of sines and cosines. Each of these sines and ...
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... points. This is one example of what is known as a structural break. Suppose we would like to test for homogeneity of variance for an observed time series. The null hypothesis is that of = 0 for all t while the alternative hypothesis is ...
... points. This is one example of what is known as a structural break. Suppose we would like to test for homogeneity of variance for an observed time series. The null hypothesis is that of = 0 for all t while the alternative hypothesis is ...
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... point for economics and finance students. Pollock (1999) presents the formulation and estimation of statistical time series models, including the design of filters and other signal processing devices. An introduction to the techniques ...
... point for economics and finance students. Pollock (1999) presents the formulation and estimation of statistical time series models, including the design of filters and other signal processing devices. An introduction to the techniques ...
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... points at the beginning and N data points at the end of each filter output are missing as a result of the centering. The sample period is January 2, 1996, to December 31, 1996 (weekends excluded). Data source: Olsen & Associates. Figure ...
... points at the beginning and N data points at the end of each filter output are missing as a result of the centering. The sample period is January 2, 1996, to December 31, 1996 (weekends excluded). Data source: Olsen & Associates. Figure ...
Página 21
... point moving average, which is used for smoothing mortality statistics, is another example of unequal filter coefficients in a noncausal FIR filter (Chatfield, 1984, page 17). The coefficients of Spencer's filter are 1 ti) = 320 (–3, -6 ...
... point moving average, which is used for smoothing mortality statistics, is another example of unequal filter coefficients in a noncausal FIR filter (Chatfield, 1984, page 17). The coefficients of Spencer's filter are 1 ti) = 320 (–3, -6 ...
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