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 4
... variables et and vst are uncorrelated Gaussian disturbance terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR(1) process in Equation 1.1 with and without ...
... variables et and vst are uncorrelated Gaussian disturbance terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR(1) process in Equation 1.1 with and without ...
Página 5
... variables with zero mean and variance o'. If we want the probability of any noise appearing in our estimate of y, to be as small as possible, as the number of samples goes to infinity, then applying the wavelet transform to y, and ...
... variables with zero mean and variance o'. If we want the probability of any noise appearing in our estimate of y, to be as small as possible, as the number of samples goes to infinity, then applying the wavelet transform to y, and ...
Página 13
... variables. Chapter 6 uses the wavelet transform to recover a “signal” from noisy observations, also known as wavelet denoising or nonparametric regression. Standard methods for deriving the threshold and selecting a thresholding rule ...
... variables. Chapter 6 uses the wavelet transform to recover a “signal” from noisy observations, also known as wavelet denoising or nonparametric regression. Standard methods for deriving the threshold and selecting a thresholding rule ...
Página 15
... variable with the underlying environment may produce complicated features. Filtering methods deal with the identification and extraction of certain features (e.g., trends, seasonalities) from a time series, which are important in terms ...
... variable with the underlying environment may produce complicated features. Filtering methods deal with the identification and extraction of certain features (e.g., trends, seasonalities) from a time series, which are important in terms ...
Página 31
... variable with zero mean and unit variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200. Although there are two ...
... variable with zero mean and unit variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200. Although there are two ...
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