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 20
... values and N future values of the input are required to obtain the filter output at time t, M data points at the beginning and N data points at the end of the output are missing. This feature makes a centered moving average less ...
... values and N future values of the input are required to obtain the filter output at time t, M data points at the beginning and N data points at the end of the output are missing. This feature makes a centered moving average less ...
Página 22
... values of the input, in addition to the current value, are required to obtain the filter output at time t. Figure 2.2 provides an illustration with the 5-min DEM-USD exchange rates for different values of M. In a simple moving average ...
... values of the input, in addition to the current value, are required to obtain the filter output at time t. Figure 2.2 provides an illustration with the 5-min DEM-USD exchange rates for different values of M. In a simple moving average ...
Página 25
... value of the coefficients decays linearly with increasing lags. (c) Geometric decay: the value of the coefficients ... values of sin 6 at different locations during a complete cycle. It starts from zero at OA (V = 0), becomes one at OB ...
... value of the coefficients decays linearly with increasing lags. (c) Geometric decay: the value of the coefficients ... values of sin 6 at different locations during a complete cycle. It starts from zero at OA (V = 0), becomes one at OB ...
Página 26
... value is one at OA, zero at OB, minus one at OC and zero at OD. However, the two functions have different starting values at OA, where the cosine function takes the value of one whereas the sine function has a value of zero. The angle 6 ...
... value is one at OA, zero at OB, minus one at OC and zero at OD. However, the two functions have different starting values at OA, where the cosine function takes the value of one whereas the sine function has a value of zero. The angle 6 ...
Página 27
... value of sin 6 or cos 6. This means that sin 6 = sin (6 + 27tn), cos 6 = cos (6 + 27tn), where n is an integer. Figure 2.5 depicts the values of two functions during two complete cycles: one counterclockwise (0 to 2t in radians) and one ...
... value of sin 6 or cos 6. This means that sin 6 = sin (6 + 27tn), cos 6 = cos (6 + 27tn), where n is an integer. Figure 2.5 depicts the values of two functions during two complete cycles: one counterclockwise (0 to 2t in radians) and one ...
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