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 ix
... WAVELET TRANSFORMS Introduction 4. l. l Fourier Transform 4. l.2 Short-Time Fourier Transform 4.1.3 Wavelet Transform Properties of the Wavelet Transform 4.2. I Continuous Wavelet Functions 4.2.2 Continuous versus Discrete Wavelet ...
... WAVELET TRANSFORMS Introduction 4. l. l Fourier Transform 4. l.2 Short-Time Fourier Transform 4.1.3 Wavelet Transform Properties of the Wavelet Transform 4.2. I Continuous Wavelet Functions 4.2.2 Continuous versus Discrete Wavelet ...
Página x
... Wavelet Packet Transform 5.3.2. The Maximal Overlap Discrete Wavelet Packet Transform 5.3.3 Example: Mexican Money Supply Wavelets and Seasonal Long Memory 5.4. | Seasonal Persistent Processes 5.4.2 Simulation of Seasonal Persistent ...
... Wavelet Packet Transform 5.3.2. The Maximal Overlap Discrete Wavelet Packet Transform 5.3.3 Example: Mexican Money Supply Wavelets and Seasonal Long Memory 5.4. | Seasonal Persistent Processes 5.4.2 Simulation of Seasonal Persistent ...
Página xiii
... transform of a signal Gain function of an ideal filter Gain functions of simple moving averages Phase shift from a simple moving average Gain functions of a first-order difference equation Volatility of DEM-USD and VaR estimates (1.650) ...
... transform of a signal Gain function of an ideal filter Gain functions of simple moving averages Phase shift from a simple moving average Gain functions of a first-order difference equation Volatility of DEM-USD and VaR estimates (1.650) ...
Página xxi
... transform. A certain level of 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 ...
... transform. A certain level of 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 ...
Página 2
... wavelet methods? Fourier series is a linear combination of sines and cosines. Each of these sines and cosines is itself a function of frequency, and therefore the Fourier transform may be seen as a decomposition on a frequency-by ...
... wavelet methods? Fourier series is a linear combination of sines and cosines. Each of these sines and cosines is itself a function of frequency, and therefore the Fourier transform may be seen as a decomposition on a frequency-by ...
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