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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Dentro del libro
Resultados 1-5 de 53
Página xi
... Spectrum Analysis 7.7. I Univariate Spectrum Analysis 7.7.2 Univariate Spectrum Estimation 7.7.3 Equivalent Degrees of Freedom for a Spectral Estimator 7.7.4 Bivariate Spectrum Analysis - 7.7.5 Bivariate Spectrum Estimation 227 228 230 ...
... Spectrum Analysis 7.7. I Univariate Spectrum Analysis 7.7.2 Univariate Spectrum Estimation 7.7.3 Equivalent Degrees of Freedom for a Spectral Estimator 7.7.4 Bivariate Spectrum Analysis - 7.7.5 Bivariate Spectrum Estimation 227 228 230 ...
Página xv
... 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 (log) U.S. Tourism Revenues Periodograms of the U.S. Unemployment, U.S. ...
... 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 (log) U.S. Tourism Revenues Periodograms of the U.S. Unemployment, U.S. ...
Página xxii
... spectrum of parametric and nonparametric filtering methods. Some of these filtering methods are widely known, whereas others, such as the wavelet methods, are fairly new to economics and finance. Our aim is to provide access to these ...
... spectrum of parametric and nonparametric filtering methods. Some of these filtering methods are widely known, whereas others, such as the wavelet methods, are fairly new to economics and finance. Our aim is to provide access to these ...
Página 7
... spectrum of the process. 1.5 SCALING It is important to understand the limitations of scaling laws because realized volatility plays an essential role in measuring volatility. There are two limitations to the precision of the estimation ...
... spectrum of the process. 1.5 SCALING It is important to understand the limitations of scaling laws because realized volatility plays an essential role in measuring volatility. There are two limitations to the precision of the estimation ...
Página 13
... spectrum. An application for testing for a change in volatility, using the IBM time series, is presented. The wavelet cross-covariance and cross-correlation are defined for multivariate time series, along with their estimators and ...
... spectrum. An application for testing for a change in volatility, using the IBM time series, is presented. The wavelet cross-covariance and cross-correlation are defined for multivariate time series, along with their estimators and ...
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