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 13
... estimator and approximate confidence intervals. The wavelet variance decomposes the variance of a time series on a ... estimators and approximate confidence intervals. The wavelet 1.8. OUTLINE | 3 1.8 Outline.
... estimator and approximate confidence intervals. The wavelet variance decomposes the variance of a time series on a ... estimators and approximate confidence intervals. The wavelet 1.8. OUTLINE | 3 1.8 Outline.
Página 14
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. along with their estimators and approximate confidence intervals. The wavelet cross-covariance decomposes the cross-covariance between two time series on a scale-by-scale basis and is ...
Ramazan Gençay, Faruk Selçuk, Brandon J. Whitcher. along with their estimators and approximate confidence intervals. The wavelet cross-covariance decomposes the cross-covariance between two time series on a scale-by-scale basis and is ...
Página 40
... estimators of daily standard deviation are given by (2.26) "Standardized return is defined as rt/ot, where rt is the log price change and ot is the standard deviation. (2.27) where 6, is an estimate of daily standard deviation,. 40 ...
... estimators of daily standard deviation are given by (2.26) "Standardized return is defined as rt/ot, where rt is the log price change and ot is the standard deviation. (2.27) where 6, is an estimate of daily standard deviation,. 40 ...
Página 52
... estimator is said to be an unbiased estimator. In this example, E(ex) = E(x-xx) 1 := pu – E # XL (x + ex-) N i =0 1. — —(Nu) = 0. Ll N' pu) = 0 Hence, the sample mean as an estimator of x is. 2A sequence of uncorrelated random variables ...
... estimator is said to be an unbiased estimator. In this example, E(ex) = E(x-xx) 1 := pu – E # XL (x + ex-) N i =0 1. — —(Nu) = 0. Ll N' pu) = 0 Hence, the sample mean as an estimator of x is. 2A sequence of uncorrelated random variables ...
Página 53
... estimator of x is an unbiased estimator. In addition to the estimation error, other criteria such as the expected absolute estimation error, E(leND = E ( x – £NI), or the expected squared estimation error, E(e)= E|a-RN)']. may be used ...
... estimator of x is an unbiased estimator. In addition to the estimation error, other criteria such as the expected absolute estimation error, E(leND = E ( x – £NI), or the expected squared estimation error, E(e)= E|a-RN)']. may be used ...
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