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 1
... Wavelets are in fact related to band-pass filters with properties similar to those used in the business-cycle literature for years. scale. This is convenient when performing tasks such as simulations, CHAPTER 1. INTRODUCTION.
... Wavelets are in fact related to band-pass filters with properties similar to those used in the business-cycle literature for years. scale. This is convenient when performing tasks such as simulations, CHAPTER 1. INTRODUCTION.
Página 15
... cycle. Burnside (1998) claimed that preferring a commonly used filtering method in macroeconomic analysis does not induce any lack of power. Niemira and Klein (1994) studied forecasting financial and economic cycles in general, and ...
... cycle. Burnside (1998) claimed that preferring a commonly used filtering method in macroeconomic analysis does not induce any lack of power. Niemira and Klein (1994) studied forecasting financial and economic cycles in general, and ...
Página 25
... cycle is completed. In a specific position such as OP, the radius makes aspecific angle 6 with OA. The sine, cosine, and tangent functions with angle 6 are defined by V V Sin 6 = –, - R h cos 6 = –, tan 6 = -. R Consider the values of ...
... cycle is completed. In a specific position such as OP, the radius makes aspecific angle 6 with OA. The sine, cosine, and tangent functions with angle 6 are defined by V V Sin 6 = –, - R h cos 6 = –, tan 6 = -. R Consider the values of ...
Página 26
... cycle: +1. Alternatively, the amplitude (size) of the fluctuation is one. The cosine function also has a constant range of fluctuations: +1. Its value is one at OA, zero at OB, minus one at OC and zero at OD. However, the two functions ...
... cycle: +1. Alternatively, the amplitude (size) of the fluctuation is one. The cosine function also has a constant range of fluctuations: +1. Its value is one at OA, zero at OB, minus one at OC and zero at OD. However, the two functions ...
Página 27
... cycles: one counterclockwise (0 to 2t in radians) and one clockwise (0 to —27 in radians). In Figure 2.5 we notice that the sine and cosine functions both have the same amplitude and the same cycle length. However, as we mentioned ...
... cycles: one counterclockwise (0 to 2t in radians) and one clockwise (0 to —27 in radians). In Figure 2.5 we notice that the sine and cosine functions both have the same amplitude and the same cycle length. However, as we mentioned ...
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