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 7
... observations. This number grows and the noise shrinks when the return measurement intervals shrink, but then the measurement bias starts to grow. Until now, the only choice was a clever trade-off between the noise and the bias, which ...
... observations. This number grows and the noise shrinks when the return measurement intervals shrink, but then the measurement bias starts to grow. Until now, the only choice was a clever trade-off between the noise and the bias, which ...
Página 8
... location of the maximum for the scale one day wavelet coefficients (observation 237). x 10° (a) DEM-USD (b) DEM-USD 1.5 –6 ! Q. Time (trading days after May 17, 1961) 2.3 FILTERS IN THE FREQUENCY DOMAIN. 8 CHAPTER 1 INTRODUCTION.
... location of the maximum for the scale one day wavelet coefficients (observation 237). x 10° (a) DEM-USD (b) DEM-USD 1.5 –6 ! Q. Time (trading days after May 17, 1961) 2.3 FILTERS IN THE FREQUENCY DOMAIN. 8 CHAPTER 1 INTRODUCTION.
Página 13
... observations, also known as wavelet denoising or nonparametric regression. Standard methods for deriving the threshold and selecting a thresholding rule are provided to facilitate easy implementation. The IBM stock prices, returns, and ...
... observations, also known as wavelet denoising or nonparametric regression. Standard methods for deriving the threshold and selecting a thresholding rule are provided to facilitate easy implementation. The IBM stock prices, returns, and ...
Página 16
... observations ordered by a time index t, where time spans from minus infinity to plus infinity, (*):-2. : (. . . , X-2, X-1, X0, X1, X2, . . .). An observed time series vector x offinite length may be viewed as one realization of a ...
... observations ordered by a time index t, where time spans from minus infinity to plus infinity, (*):-2. : (. . . , X-2, X-1, X0, X1, X2, . . .). An observed time series vector x offinite length may be viewed as one realization of a ...
Página 17
... observation (or realization) and xN-1 is the last observation. A linear filter simply converts a time series x1 into another time series y by a linear transformation, ź, — » -> yt. The outputy of a linear filter is the result of the ...
... observation (or realization) and xN-1 is the last observation. A linear filter simply converts a time series x1 into another time series y by a linear transformation, ź, — » -> yt. The outputy of a linear filter is the result of the ...
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