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.
|
Dentro del libro
Página ix
... Length Time Series 4.6.3 Boundary Conditions Applications 4.7.1 Filtering FX Intraday Seasonalities 4.7.2 Causality and Cointegration in Economics 4.7.3 Money Growth and Inflation 5 WAVELETS AND STATIONARY PROCESSES 96 97 99 99 101 101 ...
... Length Time Series 4.6.3 Boundary Conditions Applications 4.7.1 Filtering FX Intraday Seasonalities 4.7.2 Causality and Cointegration in Economics 4.7.3 Money Growth and Inflation 5 WAVELETS AND STATIONARY PROCESSES 96 97 99 99 101 101 ...
Página xiv
... lengths Le {2, 4,8} LA(8) wavelet filter coefficients The LA(8) wavelet filter in frequency domain Squared gain functions for the MB(8) and Daubechies wavelet filters Minimum-bandwidth discrete-time wavelet basis functions Time series ...
... lengths Le {2, 4,8} LA(8) wavelet filter coefficients The LA(8) wavelet filter in frequency domain Squared gain functions for the MB(8) and Daubechies wavelet filters Minimum-bandwidth discrete-time wavelet basis functions Time series ...
Página 3
... length in time. (d) Square-wave function compressed (negatively dilated) to half its original length. The wavelet transform intelligently adapts itself to capture features across a wide range offrequencies and thus has the ability to ...
... length in time. (d) Square-wave function compressed (negatively dilated) to half its original length. The wavelet transform intelligently adapts itself to capture features across a wide range offrequencies and thus has the ability to ...
Página 4
... length N = 1000 simulated AR(1) process in Equation 1.1 with and without periodic components. The ACF of the AR(1) process without seasonality (excluding XDI3 sin (27tt/P) + 0.91 st] from the simulated process) starts from a value of ...
... length N = 1000 simulated AR(1) process in Equation 1.1 with and without periodic components. The ACF of the AR(1) process without seasonality (excluding XDI3 sin (27tt/P) + 0.91 st] from the simulated process) starts from a value of ...
Página 16
... time series vector x offinite length may be viewed as one realization of a random process or a segment of an infinite sequence, Jr. -*= (..., x–2, x-1, x0, x1, x2, ..., xN-1, | 6 CHAPTER 2. LINEAR FILTERS 2.2 Filters in Time Domain.
... time series vector x offinite length may be viewed as one realization of a random process or a segment of an infinite sequence, Jr. -*= (..., x–2, x-1, x0, x1, x2, ..., xN-1, | 6 CHAPTER 2. LINEAR FILTERS 2.2 Filters in Time Domain.
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