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
... period stochastic seasonalities. The random variables et and vst are uncorrelated Gaussian disturbance terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR ...
... period stochastic seasonalities. The random variables et and vst are uncorrelated Gaussian disturbance terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR ...
Página 9
... period is December 1, 1986, through December 1, 1996. Data source: Olsen & Associates. (JPY-USD) price series for the period from December 1, 1986, to December 1, 1996. Here the volatility is defined as the absolute value of the returns ...
... period is December 1, 1986, through December 1, 1996. Data source: Olsen & Associates. (JPY-USD) price series for the period from December 1, 1986, to December 1, 1996. Here the volatility is defined as the absolute value of the returns ...
Página 20
... periods in a given business week). (d) Two weeks centered moving average, M = N = 1440. Note that M data points at the beginning and N data points at the end of each filter output are missing as a result of the centering. The sample period ...
... periods in a given business week). (d) Two weeks centered moving average, M = N = 1440. Note that M data points at the beginning and N data points at the end of each filter output are missing as a result of the centering. The sample period ...
Página 22
... period simple moving average yt (x1 + x;-1 + . . . .xt-M). | M + 1 One advantage of causal FIR filters is that there is no missing output at the end of the data sample. Only the first M output is missing since M past values of the input ...
... period simple moving average yt (x1 + x;-1 + . . . .xt-M). | M + 1 One advantage of causal FIR filters is that there is no missing output at the end of the data sample. Only the first M output is missing since M past values of the input ...
Página 23
... periods in one day so that M = 288). (c) Simple one-week moving average, M = 1440. (d) Simple two-week moving average, M = 2880. Notice that there are no missing outputs at the end of the sample, only at the beginning. Sample period is ...
... periods in one day so that M = 288). (c) Simple one-week moving average, M = 1440. (d) Simple two-week moving average, M = 2880. Notice that there are no missing outputs at the end of the sample, only at the beginning. Sample period is ...
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