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 xvii
... simulation Scaling filter coefficients for selected Daubechies extremal phase wavelets Scaling filter coefficients for selected Daubechies least asymmetric wavelets Scaling filter coefficients for selected minimum-bandwidth discrete ...
... simulation Scaling filter coefficients for selected Daubechies extremal phase wavelets Scaling filter coefficients for selected Daubechies least asymmetric wavelets Scaling filter coefficients for selected minimum-bandwidth discrete ...
Página 1
... "Wavelets are in fact related to band-pass filters with properties similar to those used in the business-cycle literature for years. scale. This is convenient when performing tasks such as simulations, CHAPTER 1. INTRODUCTION.
... "Wavelets are in fact related to band-pass filters with properties similar to those used in the business-cycle literature for years. scale. This is convenient when performing tasks such as simulations, CHAPTER 1. INTRODUCTION.
Página 2
... simulations, estimation, and testing since it is always easier to deal with an uncorrelated process as opposed to one with unknown correlation structure. These issues are studied in Chapter 5. I. I. FOURIERVERSUS WAVELET ANALYSIS At ...
... simulations, estimation, and testing since it is always easier to deal with an uncorrelated process as opposed to one with unknown correlation structure. These issues are studied in Chapter 5. I. I. FOURIERVERSUS WAVELET ANALYSIS At ...
Página 4
... simulated AR(1) process in Equation 1.1 with and without periodic components. The ACF of the AR(1) process without seasonality (excluding XDI3 sin (27tt/P) + 0.91 st] from the simulated process) starts from a value of 0.95 and decays ...
... simulated AR(1) process in Equation 1.1 with and without periodic components. The ACF of the AR(1) process without seasonality (excluding XDI3 sin (27tt/P) + 0.91 st] from the simulated process) starts from a value of 0.95 and decays ...
Página 13
... simulation and estimation (both least squares and maximum likelihood) procedures are provided for these models. A long-memory time series model is fit to IBM volatility. A generalization of the DWT, the discrete wavelet packet transform ...
... simulation and estimation (both least squares and maximum likelihood) procedures are provided for these models. A long-memory time series model is fit to IBM volatility. A generalization of the DWT, the discrete wavelet packet transform ...
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