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 2
... parameters: frequency and time shift. Since the STFT is simply applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to resolve events when they happen to fall within ...
... parameters: frequency and time shift. Since the STFT is simply applying the Fourier transform to pieces of the time series of interest, a drawback of the STFT is that it will not be able to resolve events when they happen to fall within ...
Página 7
... parameters. The null hypothesis of constant variance is rejected for the first three scales of the wavelet transform. The normalized cumulative sum of squares is displayed for the first three levels of wavelet coefficients in Figure 1.4 ...
... parameters. The null hypothesis of constant variance is rejected for the first three scales of the wavelet transform. The normalized cumulative sum of squares is displayed for the first three levels of wavelet coefficients in Figure 1.4 ...
Página 19
... parameter set ai in Equation 2.5. For example, the filter in Equation 2.6 is unstable if |a| > 1, since the impulse response diverges without limit in this case. On the other hand, if |a| < 1, the filter is stable since the impulse ...
... parameter set ai in Equation 2.5. For example, the filter in Equation 2.6 is unstable if |a| > 1, since the impulse response diverges without limit in this case. On the other hand, if |a| < 1, the filter is stable since the impulse ...
Página 39
... parameter decreases and the gain function becomes flatter around one, approximating an all pass filter. In Figure 2.11 the frequency response is presented for different values of the parameter a. The sign of the parameter a determines ...
... parameter decreases and the gain function becomes flatter around one, approximating an all pass filter. In Figure 2.11 the frequency response is presented for different values of the parameter a. The sign of the parameter a determines ...
Página 41
... A successful characterization of such data-generating processes should be estimated with models whose parameters are functions of intra- and inter-frequency dynamics. *Note that f = 1/p, where p is the period. 2.4. FILTERS IN PRACTICE 4|
... A successful characterization of such data-generating processes should be estimated with models whose parameters are functions of intra- and inter-frequency dynamics. *Note that f = 1/p, where p is the period. 2.4. FILTERS IN PRACTICE 4|
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