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 11
... increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be expected since each increase in the scale captures lower and lower frequency content in the series. Not ...
... increases (from bottom to top in the figure), the variability of the estimated wavelet cross-correlation decreases. This is to be expected since each increase in the scale captures lower and lower frequency content in the series. Not ...
Página 24
... increase and then decrease with increasing lags. A special case in economics is known as Almon lag (Almon, 1965). In an Almon lag specification, the filter coefficients may be determined according to the following second-order ...
... increase and then decrease with increasing lags. A special case in economics is known as Almon lag (Almon, 1965). In an Almon lag specification, the filter coefficients may be determined according to the following second-order ...
Página 25
... increases first, then decreases with increasing lags. Therefore, the corresponding moving average is 1 y = 05:(0.10, + 0.11x1_1 + 0.14xt_2 + 0.14xt_3 + (). 0.14xt_4 + 0.12x1–5) = 0.13x1 + 0.15xt_1 + 0.18xt_2 + 0.19xt_3 + 0.18xt_4 + 0.16 ...
... increases first, then decreases with increasing lags. Therefore, the corresponding moving average is 1 y = 05:(0.10, + 0.11x1_1 + 0.14xt_2 + 0.14xt_3 + (). 0.14xt_4 + 0.12x1–5) = 0.13x1 + 0.15xt_1 + 0.18xt_2 + 0.19xt_3 + 0.18xt_4 + 0.16 ...
Página 38
... increases with increasing f, reaching its maximum at frequency f = 1/2. Therefore, it is a high-pass filter. Finally, an IIR filter (a difference equation) may be a high-pass or low-pass filter. Consider the following IIR filter, L M ...
... increases with increasing f, reaching its maximum at frequency f = 1/2. Therefore, it is a high-pass filter. Finally, an IIR filter (a difference equation) may be a high-pass or low-pass filter. Consider the following IIR filter, L M ...
Página 40
... increase in the exchange rate is favorable. The standard deviation of the return on the DEMUSD exchange rate may be taken as an indicator of the potential change in the exchange rate. Standard RiskMetrics assumes that standardized ...
... increase in the exchange rate is favorable. The standard deviation of the return on the DEMUSD exchange rate may be taken as an indicator of the potential change in the exchange rate. Standard RiskMetrics assumes that standardized ...
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