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 xxi
... statistical background is also needed, including a basic understanding of probability theory, statistical inference, and time series analysis. Many techniques are discussed in the book, including parametric recursive and nonrecursive ...
... statistical background is also needed, including a basic understanding of probability theory, statistical inference, and time series analysis. Many techniques are discussed in the book, including parametric recursive and nonrecursive ...
Página 6
... statistical hypothesis testing is useful in detecting and locating deviations from stationarity at specific points. This is one example of what is known as a structural break. Suppose we would like to test for homogeneity of variance ...
... statistical hypothesis testing is useful in detecting and locating deviations from stationarity at specific points. This is one example of what is known as a structural break. Suppose we would like to test for homogeneity of variance ...
Página 7
... statistical significance of the volatility estimation since there are not more than a handful of independent observations. This number grows and the noise shrinks when the return measurement intervals shrink, but then the measurement ...
... statistical significance of the volatility estimation since there are not more than a handful of independent observations. This number grows and the noise shrinks when the return measurement intervals shrink, but then the measurement ...
Página 16
... statistical time series models, including the design of filters and other signal processing devices. An introduction to the techniques and theories of spectral analysis of time series can be found in Koopmans (1974) and Bloomfield (2000) ...
... statistical time series models, including the design of filters and other signal processing devices. An introduction to the techniques and theories of spectral analysis of time series can be found in Koopmans (1974) and Bloomfield (2000) ...
Página 41
... statistical processes and choose an arbitrary data frequency (e.g., one week, one month, annually etc.) claiming afterward that this particular process does a good job of capturing the dynamics of the data-generating process. In ...
... statistical processes and choose an arbitrary data frequency (e.g., one week, one month, annually etc.) claiming afterward that this particular process does a good job of capturing the dynamics of the data-generating process. 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