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 15
... prediction in natural and physical phenomeana. Kaiser and Maraval (2000) and Diebold and Rudebusch (1999) contained a detailed analysis of business cycle measurements and forecasts. work for filters that will show their applicability in ...
... prediction in natural and physical phenomeana. Kaiser and Maraval (2000) and Diebold and Rudebusch (1999) contained a detailed analysis of business cycle measurements and forecasts. work for filters that will show their applicability in ...
Página 59
... "This is an example of a problem in optimal prediction. See Sargent (1987, page 229) for a detailed exposition of this example. where SNR = o'/o: is the signal-to-noise ratio. The estimate 3.2. THE WIENER FILTER AND ESTIMATION 59.
... "This is an example of a problem in optimal prediction. See Sargent (1987, page 229) for a detailed exposition of this example. where SNR = o'/o: is the signal-to-noise ratio. The estimate 3.2. THE WIENER FILTER AND ESTIMATION 59.
Página 64
... the previous At time t = 3, • 2. + 1 X3. desired signal x plus noise et: y = x + et, (3.26). observation discarded: * 1 1 JC2 = 2* + 5)2. *The solution for the steady state value of the prediction. 64 CHAPTER 3 OPTIMUM LINEAR ESTIMATION.
... the previous At time t = 3, • 2. + 1 X3. desired signal x plus noise et: y = x + et, (3.26). observation discarded: * 1 1 JC2 = 2* + 5)2. *The solution for the steady state value of the prediction. 64 CHAPTER 3 OPTIMUM LINEAR ESTIMATION.
Página 69
... the Kalman gain. It consists of two terms: the firstonesw = p^ PW-1 +G' is the variance of predicting xN before observing yN. The other term is the variance of the observation noise 3.3. RECURSIVE FILTERING AND THE KALMAN FILTER 69.
... the Kalman gain. It consists of two terms: the firstonesw = p^ PW-1 +G' is the variance of predicting xN before observing yN. The other term is the variance of the observation noise 3.3. RECURSIVE FILTERING AND THE KALMAN FILTER 69.
Página 70
... predicting xN at time N–1. The recursive estimator is now *N = pin-1 + 6% (yN - by N-1). (3.49) After having the solution for the Kalman gain ... PREDICTION WITH THE KALN1AN FILTER The Kalman filter may 70 CHAPTER 3 OPTIMUM LINEAR ESTIMATION.
... predicting xN at time N–1. The recursive estimator is now *N = pin-1 + 6% (yN - by N-1). (3.49) After having the solution for the Kalman gain ... PREDICTION WITH THE KALN1AN FILTER The Kalman filter may 70 CHAPTER 3 OPTIMUM LINEAR ESTIMATION.
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