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 4
... terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR(1) process in Equation 1.1 with and without periodic components. The ACF of the AR(1) process without ...
... terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR(1) process in Equation 1.1 with and without periodic components. The ACF of the AR(1) process without ...
Página 10
... term traders and long-term traders. Each of these classes of traders may have its own trading tool sets consistent with its trading horizon and may possess a homogeneous appearance within its own class. Overall, it is the sum of the ...
... term traders and long-term traders. Each of these classes of traders may have its own trading tool sets consistent with its trading horizon and may possess a homogeneous appearance within its own class. Overall, it is the sum of the ...
Página 15
... terms of modeling and inference. Filtering is a universal research field used in scientific areas such as astronomy, biology, engineering, and physics, as well as in economics and finance. Traditionally, filters in economics and finance ...
... terms of modeling and inference. Filtering is a universal research field used in scientific areas such as astronomy, biology, engineering, and physics, as well as in economics and finance. Traditionally, filters in economics and finance ...
Página 20
... determined according to the successive terms from the expansionin (0.5 +0.5)*. For instance, when N = 1, the filter is y1 = 0.25xt_1 + 0.50xt + 0.25xt+1. This is known as the Hanning filter, after Julius Von. 20 CHAPTER 2 LINEAR FILTERS.
... determined according to the successive terms from the expansionin (0.5 +0.5)*. For instance, when N = 1, the filter is y1 = 0.25xt_1 + 0.50xt + 0.25xt+1. This is known as the Hanning filter, after Julius Von. 20 CHAPTER 2 LINEAR FILTERS.
Página 28
... 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 utilized in certain settings. Within the context of angular frequency, Equation 2.7 is written by ...
... 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 utilized in certain settings. Within the context of angular frequency, Equation 2.7 is written by ...
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