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 ix
... STATIONARY PROCESSES 96 97 99 99 101 101 103 106 110 112 115 117 121 124 124 125 126 130 134 135 137 138 138 142 143 143 144 144 146 146 149 156 5.3 5.4 5.5 6.3 6.4 6.5 5.2.3 Simulation of Fractional. 5. I 5.2 Introduction Wavelets and ...
... STATIONARY PROCESSES 96 97 99 99 101 101 103 106 110 112 115 117 121 124 124 125 126 130 134 135 137 138 138 142 143 143 144 144 146 146 149 156 5.3 5.4 5.5 6.3 6.4 6.5 5.2.3 Simulation of Fractional. 5. I 5.2 Introduction Wavelets and ...
Página xvi
... stationary time series Piecewise constant interpretation of the wavelet variance Wavelet variances for an AR(1) process Wavelet variance for the IBM stock price volatility Locating a change in IBM stock price volatility Monthly foreign ...
... stationary time series Piecewise constant interpretation of the wavelet variance Wavelet variances for an AR(1) process Wavelet variance for the IBM stock price volatility Locating a change in IBM stock price volatility Monthly foreign ...
Página 2
... stationary time series). However, restricting ourselves to stationary time series is not very appealing since most economic/financial time series exhibit quite complicated patterns over time (e.g., trends, abrupt changes, and volatility ...
... stationary time series). However, restricting ourselves to stationary time series is not very appealing since most economic/financial time series exhibit quite complicated patterns over time (e.g., trends, abrupt changes, and volatility ...
Página 7
... stationary. * If the structural break of interest is a possible change in the long-range dependence of the series, then all levels of wavelet coefficients should exhibit a structural change since long memory is associated with all ...
... stationary. * If the structural break of interest is a possible change in the long-range dependence of the series, then all levels of wavelet coefficients should exhibit a structural change since long memory is associated with all ...
Página 19
... for a detailed discussion of stationarity. Clements and Hendry (1999) presented a framework to study non-stationary economic time series. 7This may be seen from Equation 2.7. In that equation, 2.2. FILTERS IN TIME DOMAIN | 9.
... for a detailed discussion of stationarity. Clements and Hendry (1999) presented a framework to study non-stationary economic time series. 7This may be seen from Equation 2.7. In that equation, 2.2. FILTERS IN TIME DOMAIN | 9.
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