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 9
... plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the estimated variances for each wavelet scale and the straight lines are ordinary least squares (OLS) estimates. Each wavelet scale is associated with a ...
... plotted. In (b) and (d), the results are plotted on a log-log scale. The stars are the estimated variances for each wavelet scale and the straight lines are ordinary least squares (OLS) estimates. Each wavelet scale is associated with a ...
Página 32
... plotted in Figure 2.6. 11 An exponential signal x = Ae" may have real or complex values depending on A and a. Consider a real exponential signal, xi = Ae", where A is the initial value and r is the growth rate of xt since ln (*#- Xt-1 + ...
... plotted in Figure 2.6. 11 An exponential signal x = Ae" may have real or complex values depending on A and a. Consider a real exponential signal, xi = Ae", where A is the initial value and r is the growth rate of xt since ln (*#- Xt-1 + ...
Página 33
... plotted in Figure 2.6. If the filter coefficients are known, the frequency response may easily be calculated. For instance, if the input is the complex exponential in Equation 2.17, a three-period simple moving average y1 = w0xt + wi Xt ...
... plotted in Figure 2.6. If the filter coefficients are known, the frequency response may easily be calculated. For instance, if the input is the complex exponential in Equation 2.17, a three-period simple moving average y1 = w0xt + wi Xt ...
Página 47
... (plotted as zero) at the each end of the BK(6,32) filter output. Source: U.S. Department of Commerce, Bureau of Economic Analysis. 32 quarters by removing the trend variation, but it does not smooth out the highfrequency variations. For ...
... (plotted as zero) at the each end of the BK(6,32) filter output. Source: U.S. Department of Commerce, Bureau of Economic Analysis. 32 quarters by removing the trend variation, but it does not smooth out the highfrequency variations. For ...
Página 50
... plotted in panels (b) and (c) to make the presentation more visible. 3 OPTIMUN1 LINEAR ESTINATION 3. I INTRODUCTION In Chapter 2. 1000 2000, 3000 4000 5000 6000 7000 8000 Time Since the autocovariances are symmetric, # 2: y” t, the. 50 ...
... plotted in panels (b) and (c) to make the presentation more visible. 3 OPTIMUN1 LINEAR ESTINATION 3. I INTRODUCTION In Chapter 2. 1000 2000, 3000 4000 5000 6000 7000 8000 Time Since the autocovariances are symmetric, # 2: y” t, the. 50 ...
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