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 12
... for the wavelet cross-correlation does not include zero and therefore indicates significant multiscale correlation. For more details, see Chapter 7. |.8 OUTLINE We start with a general definition of a. |2 CHAPTER 1 INTRODUCTION.
... for the wavelet cross-correlation does not include zero and therefore indicates significant multiscale correlation. For more details, see Chapter 7. |.8 OUTLINE We start with a general definition of a. |2 CHAPTER 1 INTRODUCTION.
Página 13
... defined, along with its unbiased estimator and approximate confidence intervals. The wavelet variance decomposes the variance of a time series on a scale-by-scale basis and is related to the spectrum. An application for testing for a ...
... defined, along with its unbiased estimator and approximate confidence intervals. The wavelet variance decomposes the variance of a time series on a scale-by-scale basis and is related to the spectrum. An application for testing for a ...
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... defined as CQ tw # Xt = XL 10i Xt-i. (2.1) i=—oo * If the output of a filter F applied to xt is y, the filter is time invariant if F(xi+h) = y:4-h for all integers h (Fuller, 1976, page 151). 1 ** * — 1Ui = TMINIT, if i = 2.2. FILTERS ...
... defined as CQ tw # Xt = XL 10i Xt-i. (2.1) i=—oo * If the output of a filter F applied to xt is y, the filter is time invariant if F(xi+h) = y:4-h for all integers h (Fuller, 1976, page 151). 1 ** * — 1Ui = TMINIT, if i = 2.2. FILTERS ...
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... defined by V V Sin 6 = –, - R h cos 6 = –, tan 6 = -. R Consider the values of sin 6 at different locations during a complete cycle. It starts from zero at OA (V = 0), becomes one at OB (V = R), zero at OC angle 6 are defined by sin 6 ...
... defined by V V Sin 6 = –, - R h cos 6 = –, tan 6 = -. R Consider the values of sin 6 at different locations during a complete cycle. It starts from zero at OA (V = 0), becomes one at OB (V = R), zero at OC angle 6 are defined by sin 6 ...
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Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. angle 6 are defined by sin 6 = #. cos 6 = #, and tan 6 = #. The angle 6 is a ratio, and it is measured in degrees or radians. Let the distance between A and P on the arc be equal to the ...
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. angle 6 are defined by sin 6 = #. cos 6 = #, and tan 6 = #. The angle 6 is a ratio, and it is measured in degrees or radians. Let the distance between A and P on the arc be equal to 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