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 36
... determine the phase of the business cycle. The raw data of the IPI show that the index took the smallest value in March 1991 between January 1990 and December 1993 (see Figure 2.10a). This coincides with the trough date announced by the ...
... determine the phase of the business cycle. The raw data of the IPI show that the index took the smallest value in March 1991 between January 1990 and December 1993 (see Figure 2.10a). This coincides with the trough date announced by the ...
Página 51
... determination of the optimal coefficients of an FIR filter assuming a specific class of observations, yf = x t + ét, (3.1) where y is the observation, et is the noise, and x is the desired signal from the realization of a random process ...
... determination of the optimal coefficients of an FIR filter assuming a specific class of observations, yf = x t + ét, (3.1) where y is the observation, et is the noise, and x is the desired signal from the realization of a random process ...
Página 54
... determining the weights of a linear filter optimally was carried out by Wiener (1949), and a linear filter based on ... determination, as well as in seismic data processing. An early advanced book that exclusively covers the Kalman ...
... determining the weights of a linear filter optimally was carried out by Wiener (1949), and a linear filter based on ... determination, as well as in seismic data processing. An early advanced book that exclusively covers the Kalman ...
Página 55
... determining the optimum weights is the minimum MSE, which may allow a biased estimator if the variance of the estimator is significantly lower: - min E(e.) min E |es - w)" *} ti) 2 N–1 min E (.. - XL wi(xN-i + •) . (3.6) i=0 The minimum ...
... determining the optimum weights is the minimum MSE, which may allow a biased estimator if the variance of the estimator is significantly lower: - min E(e.) min E |es - w)" *} ti) 2 N–1 min E (.. - XL wi(xN-i + •) . (3.6) i=0 The minimum ...
Página 58
... determine the variations in real wage. Assume that the disturbances are serially uncorrelated processes with u ~ N(0, 0') and v - N(0, o'). Furthermore suppose that the disturbances are not correlated with each other or with z. The ...
... determine the variations in real wage. Assume that the disturbances are serially uncorrelated processes with u ~ N(0, 0') and v - N(0, o'). Furthermore suppose that the disturbances are not correlated with each other or with z. The ...
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