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 viii
... Noise Ratio 3.2.3 Comments on Wiener Filtering and Estimation 3.2.4 Pitfalls of the Sample Autocorrelation and CrossCorrelation Recursive Filtering and the Kalman Filter 3.3.1 Recursive Mean Estimation 3.3.2. The Kalman Filter and ...
... Noise Ratio 3.2.3 Comments on Wiener Filtering and Estimation 3.2.4 Pitfalls of the Sample Autocorrelation and CrossCorrelation Recursive Filtering and the Kalman Filter 3.3.1 Recursive Mean Estimation 3.3.2. The Kalman Filter and ...
Página xiii
... Noise filtering with universal and minimax estimators Identification of structural breaks and IBM volatility Scaling of foreign exchange volatility across time horizons Multiresolution analysis of foreign exchange volatility Wavelet ...
... Noise filtering with universal and minimax estimators Identification of structural breaks and IBM volatility Scaling of foreign exchange volatility across time horizons Multiresolution analysis of foreign exchange volatility Wavelet ...
Página 5
... noise model; that is, yi = St + ét, t = 0, 1, . . . , N – 1. (1.2) For now letus assumes, is a deterministic function of t and associated with lower frequency oscillations (i.e., it is relatively smooth). Let us also assume that the noise ...
... noise model; that is, yi = St + ét, t = 0, 1, . . . , N – 1. (1.2) For now letus assumes, is a deterministic function of t and associated with lower frequency oscillations (i.e., it is relatively smooth). Let us also assume that the noise ...
Página 6
... noise. The true function (dotted line) is drawn in the bottom two panels for comparison with the estimate. When developing time series models, a natural assumption is that of (secondorder) stationarity. That is, the time series model ...
... noise. The true function (dotted line) is drawn in the bottom two panels for comparison with the estimate. When developing time series models, a natural assumption is that of (secondorder) stationarity. That is, the time series model ...
Página 7
... noise shrinks when the return measurement intervals shrink, but then the measurement bias starts to grow. Until now, the only choice was a clever trade-off between the noise and the bias, which led to typical return intervals of about ...
... noise shrinks when the return measurement intervals shrink, but then the measurement bias starts to grow. Until now, the only choice was a clever trade-off between the noise and the bias, which led to typical return intervals of about ...
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