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 xvii
... dynamic range for the spectra of DWT wavelet coefficients Minimax thresholds Results of testing IBM stock volatility for homogeneity of variance Wavelet estimates of betas for different companies Out-of-sample MSPE of the S&P-500 call ...
... dynamic range for the spectra of DWT wavelet coefficients Minimax thresholds Results of testing IBM stock volatility for homogeneity of variance Wavelet estimates of betas for different companies Out-of-sample MSPE of the S&P-500 call ...
Página xxi
... For neural network methods, there is emphasis on the dynamic architectures (such as recurrent networks) in addition to simple feedforward networks. Recurrent networks together with multistream learning provide a xxi PREFACE.
... For neural network methods, there is emphasis on the dynamic architectures (such as recurrent networks) in addition to simple feedforward networks. Recurrent networks together with multistream learning provide a xxi PREFACE.
Página 1
... dynamics of economic/financial time series beyond that of current methodology. A number of concepts such as ... dynamic properties of a process at these timescales." Last but not least, wavelet filters provide a convenient way of ...
... dynamics of economic/financial time series beyond that of current methodology. A number of concepts such as ... dynamic properties of a process at these timescales." Last but not least, wavelet filters provide a convenient way of ...
Página 3
... dynamics. Specifically, the periodic component pulls the calculated autocorrelations down, giving the impression that there is . – AF1 * Wavele! Srriorith \ - AR1 = 0 50 100 150 200 250 300 0 50 100 150 200 250 300 no persistence other ...
... dynamics. Specifically, the periodic component pulls the calculated autocorrelations down, giving the impression that there is . – AF1 * Wavele! Srriorith \ - AR1 = 0 50 100 150 200 250 300 0 50 100 150 200 250 300 no persistence other ...
Página 5
... dynamics and the dotted lines are the ACF of the seasonally adjusted series using a wavelet multiresolution analysis. As Figure 1.2 displays, using a multiresolution analysis to selectively filter a time series successfully uncovers the ...
... dynamics and the dotted lines are the ACF of the seasonally adjusted series using a wavelet multiresolution analysis. As Figure 1.2 displays, using a multiresolution analysis to selectively filter a time series successfully uncovers the ...
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