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
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Página xiii
... Phase shift from a simple moving average Gain functions of a first-order difference equation Volatility of DEM-USD and VaR estimates (1.650) Gain functions of an EWMA and a simple moving average Gain functions of the Hodrick-Prescott ...
... Phase shift from a simple moving average Gain functions of a first-order difference equation Volatility of DEM-USD and VaR estimates (1.650) Gain functions of an EWMA and a simple moving average Gain functions of the Hodrick-Prescott ...
Página xiv
... Phase diagram for the OLG pricing function Multiresolution analysis of OLG price series (6 = 3.1) Multiresolution analysis of OLG price series (6 = 4) MODWT decompositions of the IBM return series Sum of squared MODWT coefficients for ...
... Phase diagram for the OLG pricing function Multiresolution analysis of OLG price series (6 = 3.1) Multiresolution analysis of OLG price series (6 = 4) MODWT decompositions of the IBM return series Sum of squared MODWT coefficients for ...
Página xvii
... phase wavelets Scaling filter coefficients for selected Daubechies least asymmetric wavelets Scaling filter coefficients for selected minimum-bandwidth discrete time wavelets Unit-root test results for raw and first differenced series ...
... phase wavelets Scaling filter coefficients for selected Daubechies least asymmetric wavelets Scaling filter coefficients for selected minimum-bandwidth discrete time wavelets Unit-root test results for raw and first differenced series ...
Página 13
... phase, and gain – are introduced in Chapter 2. Chapter 2 ends with three applications. Chapter3 views filtering as an optimum linearestimation problem. After reviewing the Wiener filter, the Kalman filter is introduced. The derivation ...
... phase, and gain – are introduced in Chapter 2. Chapter 2 ends with three applications. Chapter3 views filtering as an optimum linearestimation problem. After reviewing the Wiener filter, the Kalman filter is introduced. The derivation ...
Página 27
... phase (i.e., the location of the peak and the trough of the oscillation in each function is different in each period). 27 to any angle 6 would not change the value of sin 6 or cos 6. This means that sin 6 = sin (6 + 27tn), cos 6 = cos ...
... phase (i.e., the location of the peak and the trough of the oscillation in each function is different in each period). 27 to any angle 6 would not change the value of sin 6 or cos 6. This means that sin 6 = sin (6 + 27tn), cos 6 = cos ...
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 |
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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