An Introduction to Wavelets and Other Filtering Methods in Finance and EconomicsAn 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 multiresolution 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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... XDI3 sin (27tt/P) + 0.91 st] from the simulated process) starts from a value of 0.95 and decays geometrically. ... ACF of the AR(1) process with the seasonality starts from 0.40 and fluctuates between positive and negative values.
... XDI3 sin (27tt/P) + 0.91 st] from the simulated process) starts from a value of 0.95 and decays geometrically. ... ACF of the AR(1) process with the seasonality starts from 0.40 and fluctuates between positive and negative values.
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Here the volatility is defined as the absolute value of the returns. Our results provide evidence that the scaling behavior of volatility breaks at scales higher than one day. Figure 1.5 reports the decomposition of the variance on a ...
Here the volatility is defined as the absolute value of the returns. Our results provide evidence that the scaling behavior of volatility breaks at scales higher than one day. Figure 1.5 reports the decomposition of the variance on a ...
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(2.2) i=—co where future values of xt are required to obtain the filter output at time t. This may not be feasible in certain applications. As a result, some restrictions are imposed on a filter such that the output of the filter is not ...
(2.2) i=—co where future values of xt are required to obtain the filter output at time t. This may not be feasible in certain applications. As a result, some restrictions are imposed on a filter such that the output of the filter is not ...
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... linear difference equations, L M y =XLay: +X w.xi, (2.5) i=1 i=0 where L lagged values of output y, and M lagged values of input xt, as well as the current value of the input, are employed to determine the current value of output.
... linear difference equations, L M y =XLay: +X w.xi, (2.5) i=1 i=0 where L lagged values of output y, and M lagged values of input xt, as well as the current value of the input, are employed to determine the current value of output.
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2.2.2 Noncausal Finite Impulse Response (FIR) Filters The general form of an FIR filter is M y1 = X tl'i Xti, i =–N where the filter processes N future and M past values as well as the current value of the input.
2.2.2 Noncausal Finite Impulse Response (FIR) Filters The general form of an FIR filter is M y1 = X tl'i Xti, i =–N where the filter processes N future and M past values as well as the current value of the input.
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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 beta calculated components computed correlation covariance cycle decomposition defined determined difference discrete distribution dynamics Equation error estimator example feedforward network Figure Fourier transform frequency function gain function Gaussian given Haar hidden units increases indicate input interval Kalman filter 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 values variables variance vector volatility wavelet coefficients wavelet details wavelet filter wavelet scale wavelet transform wavelet variance weights zero