An Introduction to Wavelets and Other Filtering Methods in Finance and EconomicsAn 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 multiresolution 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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I 4.2 4.3 4.4 4.5 4.6 4.7 4 DISCRETE WAVELET TRANSFORMS Introduction 4. l. l Fourier Transform 4. l.2 ShortTime Fourier Transform 4.1.3 Wavelet Transform Properties of the Wavelet Transform 4.2. I Continuous Wavelet Functions 4.2.2 ...
I 4.2 4.3 4.4 4.5 4.6 4.7 4 DISCRETE WAVELET TRANSFORMS Introduction 4. l. l Fourier Transform 4. l.2 ShortTime Fourier Transform 4.1.3 Wavelet Transform Properties of the Wavelet Transform 4.2. I Continuous Wavelet Functions 4.2.2 ...
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The Discrete 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 ...
The Discrete 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 ...
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... simple moving averages Filter coefficients in a simple moving average A circle with radius R Cyclical functions Time series of representations of cyclical functions Fourier transform of a signal Gain function of an ideal filter Gain ...
... simple moving averages Filter coefficients in a simple moving average A circle with radius R Cyclical functions Time series of representations of cyclical functions Fourier transform of a signal Gain function of an ideal filter Gain ...
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Regardless of one's profession, this book assumes a basic understanding of mathematics, including such topics as trigonometry, basic linear algrebra, calculus, and the Fourier transform. A certain level of statistical background is also ...
Regardless of one's profession, this book assumes a basic understanding of mathematics, including such topics as trigonometry, basic linear algrebra, calculus, and the Fourier transform. A certain level of statistical background is also ...
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Hence, a transform that decomposes a process into different time horizons is appealing as it differentiates seasonalities, reveals structural breaks and volatility clusters, and identifies local and global dynamic properties of a ...
Hence, a transform that decomposes a process into different time horizons is appealing as it differentiates seasonalities, reveals structural breaks and volatility clusters, and identifies local and global dynamic properties of a ...
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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