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 xiv
... Gaussian probability density function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet approximations Haar wavelet filter coefficients The Haar wavelet filter in frequency domain ...
... Gaussian probability density function Critical sampling of the time-frequency plane Squared gain functions for ideal filters and their wavelet approximations Haar wavelet filter coefficients The Haar wavelet filter in frequency domain ...
Página 4
... 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 periodic components. The ACF of the AR ...
... 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 periodic components. The ACF of the AR ...
Página 5
... 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 applying the wavelet transform ...
... 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 applying the wavelet transform ...
Página 7
... 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 of the wavelet transform ...
... 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 of the wavelet transform ...
Página 52
... Gaussian white noise. *In this chapter, we assume that observations start at 1 and end at N in agreement with the generally accepted notation in the Kalman filter literature. 4 Specifically, an estimator it of a parameter u is 52 ...
... Gaussian white noise. *In this chapter, we assume that observations start at 1 and end at N in agreement with the generally accepted notation in the Kalman filter literature. 4 Specifically, an estimator it of a parameter u is 52 ...
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 |
Otras ediciones - Ver todas
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