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
... original time series such that it summarizes information in the data as a function of frequency and therefore does not preserve information in time. This is the opposite of how we observe the original time series, where no frequency ...
... original time series such that it summarizes information in the data as a function of frequency and therefore does not preserve information in time. This is the opposite of how we observe the original time series, where no frequency ...
Página 3
... original length in time. (d) Square-wave function compressed (negatively dilated) to half its original length. The wavelet transform intelligently adapts itself to capture features across a wide range offrequencies and thus has the ...
... original length in time. (d) Square-wave function compressed (negatively dilated) to half its original length. The wavelet transform intelligently adapts itself to capture features across a wide range offrequencies and thus has the ...
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... original IBM volatility series, and the following three rows are the normalized cumulative sum of squares (NCSS) of the first three levels of its wavelet decomposition. These three levels are associate with changes in longer and longer ...
... original IBM volatility series, and the following three rows are the normalized cumulative sum of squares (NCSS) of the first three levels of its wavelet decomposition. These three levels are associate with changes in longer and longer ...
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... original data. (b) One-day centered moving average with M = N = 144 (there are 288 5-min periods in a given day). (c) One-week centered moving average, M = N = 720 (there are 1440.5-min periods in a given business week). (d) Two weeks ...
... original data. (b) One-day centered moving average with M = N = 144 (there are 288 5-min periods in a given day). (c) One-week centered moving average, M = N = 720 (there are 1440.5-min periods in a given business week). (d) Two weeks ...
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... original input x1, the first step is to filter the input by using the first set of filter coefficients w1: M Zf E. XD. 101,i Xt—i. i=-N At the second stage, the first stage filter output z is filtered once again by using the second set of ...
... original input x1, the first step is to filter the input by using the first set of filter coefficients w1: M Zf E. XD. 101,i Xt—i. i=-N At the second stage, the first stage filter output z is filtered once again by using the second set of ...
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 |
Otras ediciones - Ver todas
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