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 xxii
... known, whereas others, such as the wavelet methods, are fairly new to economics and finance. Our aim is to provide access to these methods that can be easily followed. Ramazan Gençay Faruk Selçuk Brandon Whitcher INTRODUCTION The ...
... known, whereas others, such as the wavelet methods, are fairly new to economics and finance. Our aim is to provide access to these methods that can be easily followed. Ramazan Gençay Faruk Selçuk Brandon Whitcher INTRODUCTION The ...
Página 6
... known as a structural break. Suppose we would like to test for homogeneity of variance for an observed time series. The null hypothesis is that of = 0 for all t while the alternative hypothesis is, for example, o? - of for t < k and o ...
... known as a structural break. Suppose we would like to test for homogeneity of variance for an observed time series. The null hypothesis is that of = 0 for all t while the alternative hypothesis is, for example, o? - of for t < k and o ...
Página 10
... known as the wavelet variance, to the periodogram. This variance decomposition may be easily generalized for multivariate time series. Standard time-domain measures of association for multivariate time series (e.g., cross-covariance and ...
... known as the wavelet variance, to the periodogram. This variance decomposition may be easily generalized for multivariate time series. Standard time-domain measures of association for multivariate time series (e.g., cross-covariance and ...
Página 13
... known as wavelet denoising or nonparametric regression. Standard methods for deriving the threshold and selecting a thresholding rule are provided to facilitate easy implementation. The IBM stock prices, returns, and volatility are ...
... known as wavelet denoising or nonparametric regression. Standard methods for deriving the threshold and selecting a thresholding rule are provided to facilitate easy implementation. The IBM stock prices, returns, and volatility are ...
Página 15
... known as the Beveridge-Nelson procedure (Beveridge and Nelson, 1981). Another well-known method is the Hodrick-Prescott filter(Hodrick and Prescott, 1997). Canova (1998a, 1998b) critically evaluated a set of popular filters used in ...
... known as the Beveridge-Nelson procedure (Beveridge and Nelson, 1981). Another well-known method is the Hodrick-Prescott filter(Hodrick and Prescott, 1997). Canova (1998a, 1998b) critically evaluated a set of popular filters used in ...
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