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 29
... stationary. If the sequence is not stationary, it can be made stationary by different methods as discussed in Hamilton (1994b, Ch. 3). 9 A complex number z may be expressed in different forms. The Cartesian or rectangular form for z is ...
... stationary. If the sequence is not stationary, it can be made stationary by different methods as discussed in Hamilton (1994b, Ch. 3). 9 A complex number z may be expressed in different forms. The Cartesian or rectangular form for z is ...
Página 34
... function of stationary time series; only the filter is a deterministic function, while the time series is stochastic. *Total Production Index, 1992 = 100, Monthly, 1990:01-1993:12, Seasonally Not. 34 CHAPTER 2. LINEAR FILTERS.
... function of stationary time series; only the filter is a deterministic function, while the time series is stochastic. *Total Production Index, 1992 = 100, Monthly, 1990:01-1993:12, Seasonally Not. 34 CHAPTER 2. LINEAR FILTERS.
Página 45
... stationary." Pollock (2000) proposed a rational square-wave filter approach to detrending and points out that the HP filter fails in the task of generating a detrended series by allowing powerful low-frequency components to pass through ...
... stationary." Pollock (2000) proposed a rational square-wave filter approach to detrending and points out that the HP filter fails in the task of generating a detrended series by allowing powerful low-frequency components to pass through ...
Página 54
... stationary. See Hamilton (1994b, Ch. 3) for a detailed discussion of the stationarity. Clements and Hendry (1999) presented a framework to study nonstationary economic time series. *In a similar setup, Hamilton (1994b, Ch. 2) considered ...
... stationary. See Hamilton (1994b, Ch. 3) for a detailed discussion of the stationarity. Clements and Hendry (1999) presented a framework to study nonstationary economic time series. *In a similar setup, Hamilton (1994b, Ch. 2) considered ...
Página 55
... stationary linear process with a Wold representation, which is an IIR filter with white noise inputs. ”Two vectors, a and b, are orthogonal if and only if a" b = b a = 0. Two random variables x and y are said to be orthogonal if E(xy) ...
... stationary linear process with a Wold representation, which is an IIR filter with white noise inputs. ”Two vectors, a and b, are orthogonal if and only if a" b = b a = 0. Two random variables x and y are said to be orthogonal if E(xy) ...
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