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
... indicate that the wavelet coefficients at one level are not (much) associated with coefficients at different scales or within their "Wavelets are in fact related to band-pass filters with properties similar to those used in the business ...
... indicate that the wavelet coefficients at one level are not (much) associated with coefficients at different scales or within their "Wavelets are in fact related to band-pass filters with properties similar to those used in the business ...
Página 12
... indicate the approximate 95% confidence interval for the wavelet cross-correlation. At each wavelet scale, there are ... indicates significant multiscale correlation. For more details, see Chapter 7. |.8 OUTLINE We start with a general ...
... indicate the approximate 95% confidence interval for the wavelet cross-correlation. At each wavelet scale, there are ... indicates significant multiscale correlation. For more details, see Chapter 7. |.8 OUTLINE We start with a general ...
Página 28
... indicates that each month has f = 1/12 cyclical oscillation. The definition of frequency in terms of the number of cycles per time period is easy to understand and interpret. However, the angular frequency, a = 2n f, may also be ...
... indicates that each month has f = 1/12 cyclical oscillation. The definition of frequency in terms of the number of cycles per time period is easy to understand and interpret. However, the angular frequency, a = 2n f, may also be ...
Página 31
... indicates that the relative weights of these periodic components are much higher than any other component of the signal. 2.3.1 Frequency Response Impulse response function in the time domain is a useful tool for describing and ...
... indicates that the relative weights of these periodic components are much higher than any other component of the signal. 2.3.1 Frequency Response Impulse response function in the time domain is a useful tool for describing and ...
Página 32
... indicates that the relative weights of the components with 12 and 20 period oscillations are much higher than any other component of the signal. where i = V-1, f is the frequency defined earlier and we is the impulse response function ...
... indicates that the relative weights of the components with 12 and 20 period oscillations are much higher than any other component of the signal. where i = V-1, f is the frequency defined earlier and we is the impulse response function ...
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