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.
|
Dentro del libro
Resultados 1-5 de 55
Página 31
... distributed random variable with zero mean and unit variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200 ...
... distributed random variable with zero mean and unit variance. This time series has two cyclical components with period lengths of 12 and 20 since f = 1/12 and f2 = 1/20. Figure 2.7a plots a sample of this time series with N = 200 ...
Página 40
... distributed." Therefore,. given. a. value. of. the. daily. standard. deviation, a potential drop in the exchange rate in one ... distribution is 1.65). This means that the exchange rate is not expected to decrease more than 0.825% in one day ...
... distributed." Therefore,. given. a. value. of. the. daily. standard. deviation, a potential drop in the exchange rate in one ... distribution is 1.65). This means that the exchange rate is not expected to decrease more than 0.825% in one day ...
Página 41
... distributed, the VaR estimate is 1.65 times the estimated standard deviation. Notice that the EWMA estimate of the VaRcaptures the jump and laterfall in volatility around the 110th day much more successfully relative to the simple ...
... distributed, the VaR estimate is 1.65 times the estimated standard deviation. Notice that the EWMA estimate of the VaRcaptures the jump and laterfall in volatility around the 110th day much more successfully relative to the simple ...
Página 51
... distributed with constant mean u and variance o', denoted by x ~ (l, o?)." Therefore, the observation of this signal at time t is where observation noise et is uncorrelated and distributed with zero. y = x + et, (3.2) | This assumption ...
... distributed with constant mean u and variance o', denoted by x ~ (l, o?)." Therefore, the observation of this signal at time t is where observation noise et is uncorrelated and distributed with zero. y = x + et, (3.2) | This assumption ...
Página 52
... distributed with zero mean and constant variance, denoted by et ~ (0, o?).” Filtering in this context can be viewed as extracting (or estimating) x from the noisy observation yi. Given the observation set,” y = (y1, y2, y3, ... , yN), a ...
... distributed with zero mean and constant variance, denoted by et ~ (0, o?).” Filtering in this context can be viewed as extracting (or estimating) x from the noisy observation yi. Given the observation set,” y = (y1, y2, y3, ... , yN), a ...
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 |
Otras ediciones - Ver todas
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