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 36
Página xxi
... presentation contains testing issues that can be performed using wavelets with multiresolution analysis. For neural network methods, there is emphasis on the dynamic architectures (such as recurrent networks) in addition to simple ...
... presentation contains testing issues that can be performed using wavelets with multiresolution analysis. For neural network methods, there is emphasis on the dynamic architectures (such as recurrent networks) in addition to simple ...
Página xxii
... presented with high-frequency financial time series. This provides a platform for the usefulness of the wavelet methods in the analysis of intraday seasonality, identification of trader behavior at different trading horizons, and the ...
... presented with high-frequency financial time series. This provides a platform for the usefulness of the wavelet methods in the analysis of intraday seasonality, identification of trader behavior at different trading horizons, and the ...
Página 13
... IBM time series, is presented. The wavelet cross-covariance and cross-correlation are defined for multivariate time series, along with their estimators and approximate confidence intervals. The wavelet 1.8. OUTLINE | 3 1.8 Outline.
... IBM time series, is presented. The wavelet cross-covariance and cross-correlation are defined for multivariate time series, along with their estimators and approximate confidence intervals. The wavelet 1.8. OUTLINE | 3 1.8 Outline.
Página 19
... (1994b, Ch. 3) for a detailed discussion of stationarity. Clements and Hendry (1999) presented a framework to study non-stationary economic time series. 7This may be seen from Equation 2.7. In that equation, 2.2. FILTERS IN TIME DOMAIN | 9.
... (1994b, Ch. 3) for a detailed discussion of stationarity. Clements and Hendry (1999) presented a framework to study non-stationary economic time series. 7This may be seen from Equation 2.7. In that equation, 2.2. FILTERS IN TIME DOMAIN | 9.
Página 39
... presented for different values of the parameter a. The sign of the parameter a determines whether the linear difference equation in Equation 2.25 is a low-pass filter as in the left panel or a high-pass filteras in the right panel of ...
... presented for different values of the parameter a. The sign of the parameter a determines whether the linear difference equation in Equation 2.25 is a low-pass filter as in the left panel or a high-pass filteras in the right panel of ...
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