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 69
Página x
... Approximate Maximum Likelihood Estimation of Fractional Difference Processes 5.2.6 Example: IBM Stock Prices Generalizations of the DWT and MODWT 5.3. The Discrete Wavelet Packet Transform 5.3.2. The Maximal Overlap Discrete Wavelet ...
... Approximate Maximum Likelihood Estimation of Fractional Difference Processes 5.2.6 Example: IBM Stock Prices Generalizations of the DWT and MODWT 5.3. The Discrete Wavelet Packet Transform 5.3.2. The Maximal Overlap Discrete Wavelet ...
Página xv
... Approximate log-linear relationship between the spectra of SPPS Estimated spectrum of money supply for Mexico Multiresolution analysis of the Japanese GDE Estimated spectrum of the quarterly Japanese GDE U.S. Unemployment, U.S. CPI, and ...
... Approximate log-linear relationship between the spectra of SPPS Estimated spectrum of money supply for Mexico Multiresolution analysis of the Japanese GDE Estimated spectrum of the quarterly Japanese GDE U.S. Unemployment, U.S. CPI, and ...
Página 1
... approximate decorrelation emerge from wavelet filters. Wavelet filtering provides a natural platform to deal with the time-varying characteristics found in most real-world time series, and thus the assumption of stationarity may be ...
... approximate decorrelation emerge from wavelet filters. Wavelet filtering provides a natural platform to deal with the time-varying characteristics found in most real-world time series, and thus the assumption of stationarity may be ...
Página 11
... Approximate 95% confidence intervals are given so that significant cross-correlations may be easily identified. As the wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross ...
... Approximate 95% confidence intervals are given so that significant cross-correlations may be easily identified. As the wavelet scale increases (from bottom to top in the figure), the variability of the estimated wavelet cross ...
Página 12
... approximate 95% confidence interval for the wavelet cross-correlation. At each wavelet scale, there are several lags very close to zero where the confidence interval for the wavelet cross-correlation does not include zero and therefore ...
... approximate 95% confidence interval for the wavelet cross-correlation. At each wavelet scale, there are several lags very close to zero where the confidence interval for the wavelet cross-correlation does not include zero and therefore ...
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