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.
|
Dentro del libro
Resultados 1-5 de 89
Página 4
... 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(1) process without seasonality ...
... 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(1) process without seasonality ...
Página 5
... 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 thresholding the ...
... 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 thresholding the ...
Página 11
... zero, and the sixth scale again exhibits its maximum at lag zero. More interesting features are, for example, the asymmetry in the wavelet cross-correlation sequence for scales 4 and 5. At the fourth scale (associated with oscillations ...
... zero, and the sixth scale again exhibits its maximum at lag zero. More interesting features are, for example, the asymmetry in the wavelet cross-correlation sequence for scales 4 and 5. At the fourth scale (associated with oscillations ...
Página 12
... zero where the confidence interval 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.
... zero where the confidence interval 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.
Página 19
... zero. On the other hand, if the system is not stable, the impulse response will diverge. The stability of the difference equations depends on the parameter set ai in Equation 2.5. For example, the filter in Equation 2.6 is unstable if ...
... zero. On the other hand, if the system is not stable, the impulse response will diverge. The stability of the difference equations depends on the parameter set ai in Equation 2.5. For example, the filter in Equation 2.6 is unstable if ...
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