An Introduction to Wavelets and Other Filtering Methods in Finance and Economics
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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CHAPTER 7 WAVELETS FOR VARIANCECOVARIANCE ESTIMATION
CHAPTER 8 ARTIFICIAL NEURAL NETWORKS
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analysis applied approximate associated assumed basis beta calculated components computed correlation covariance cycle decomposition defined determined difference discrete distribution dynamics Equation error estimator example feedforward network Figure Fourier transform frequency function gain function Gaussian given Haar hidden units increases indicate input interval Kalman filter 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 values variables variance vector volatility wavelet coefficients wavelet details wavelet filter wavelet scale wavelet transform wavelet variance weights zero