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
... periodic components: |.4 IDENTIFICATION OF STRUCTURAL BREAKS Time (trading days after May. *The autocorrelation coefficient is 0.95% where k is the number of lags. *The convolution of two sequences such as xt and wi. 4 CHAPTER 1 ...
... periodic components: |.4 IDENTIFICATION OF STRUCTURAL BREAKS Time (trading days after May. *The autocorrelation coefficient is 0.95% where k is the number of lags. *The convolution of two sequences such as xt and wi. 4 CHAPTER 1 ...
Página 5
... sequence of uncorrelated Gaussian random 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 ...
... sequence of uncorrelated Gaussian random 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 ...
Página 7
... sequence of uncorrelated Gaussian random variables (required by CUSUM procedures), nor can it be effectively modeled by an ARMA process with few parameters. The null hypothesis of constant variance is rejected for the first three scales ...
... sequence of uncorrelated Gaussian random variables (required by CUSUM procedures), nor can it be effectively modeled by an ARMA process with few parameters. The null hypothesis of constant variance is rejected for the first three scales ...
Página 11
... sequence for scales 4 and 5. At the fourth scale (associated with oscillations of 16 to 32 months), the DEM-USD exchange rate is negatively correlated with the JPY-USD at a lag of +8 months but not at a lag of –8 months. This feature ...
... sequence for scales 4 and 5. At the fourth scale (associated with oscillations of 16 to 32 months), the DEM-USD exchange rate is negatively correlated with the JPY-USD at a lag of +8 months but not at a lag of –8 months. This feature ...
Página 16
... sequence of 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 ...
... sequence of 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 ...
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