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 35
... shows that the gain function is cos(tf) and the phase is "t f. Since the phase of the filter is not zero, there will be a change in the phase of the original time series. This is known as the phase shift. The phase shift has important ...
... shows that the gain function is cos(tf) and the phase is "t f. Since the phase of the filter is not zero, there will be a change in the phase of the original time series. This is known as the phase shift. The phase shift has important ...
Página 36
... 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 NBER. However, an eight-period simple moving average of the IPI in ...
... 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 NBER. However, an eight-period simple moving average of the IPI in ...
Página 37
... show that the index took the smallest value in March 1991 while an eightperiod simple moving average in (c) indicates that the trough was in July 1991. There is a four-period phase shift after filtering with an eight-period simple ...
... show that the index took the smallest value in March 1991 while an eightperiod simple moving average in (c) indicates that the trough was in July 1991. There is a four-period phase shift after filtering with an eight-period simple ...
Página 41
... shows abrupt movements in volatility once the large observation falls out of the averaging period.” To illustrate, Figure 2.12 plots the volatility of the daily DEM-USD exchange rate and VaRestimates from June 6, 1997, to December 31 ...
... shows abrupt movements in volatility once the large observation falls out of the averaging period.” To illustrate, Figure 2.12 plots the volatility of the daily DEM-USD exchange rate and VaRestimates from June 6, 1997, to December 31 ...
Página 48
... shows that employing simple technical trading rules may improve the forecasting performance of existing models. Also, an optimization of technical trading strategies with neural network models may result in some profitability in ...
... shows that employing simple technical trading rules may improve the forecasting performance of existing models. Also, an optimization of technical trading strategies with neural network models may result in some profitability in ...
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