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 viii
... Simulation 3.6.2 Vector Kalman Filter Simulation 3.6.3 Bayesian Vector Autoregression 3.6.4 Time-Varying Beta Estimation 15 16 18 19 22 25 31 34 39 40 44 46 48 51 54 58 59 60 61 63 65 67 71 72 73 74 76 77 78 78 79 80 80 83 84 90 4. I ...
... Simulation 3.6.2 Vector Kalman Filter Simulation 3.6.3 Bayesian Vector Autoregression 3.6.4 Time-Varying Beta Estimation 15 16 18 19 22 25 31 34 39 40 44 46 48 51 54 58 59 60 61 63 65 67 71 72 73 74 76 77 78 78 79 80 80 83 84 90 4. I ...
Página ix
... Simulation of Fractional. 5. I 5.2 Introduction Wavelets and Long-Memory Processes 5.2. I Fractional Difference Processes 5.2.2 The DWT of Fractional Difference Processes 161 162 163 164 5.19 6. I 6.2 6.3 6.4 6.5 6.6 6.7 6.8 CONTENTS ix.
... Simulation of Fractional. 5. I 5.2 Introduction Wavelets and Long-Memory Processes 5.2. I Fractional Difference Processes 5.2.2 The DWT of Fractional Difference Processes 161 162 163 164 5.19 6. I 6.2 6.3 6.4 6.5 6.6 6.7 6.8 CONTENTS ix.
Página x
... Simulation of Fractional Difference Processes 5.2.4 Ordinary Least-Squares Estimation of Fractional Difference Processes 5.2.5 Approximate Maximum Likelihood Estimation of Fractional Difference Processes 5.2.6 Example: IBM Stock Prices ...
... Simulation of Fractional Difference Processes 5.2.4 Ordinary Least-Squares Estimation of Fractional Difference Processes 5.2.5 Approximate Maximum Likelihood Estimation of Fractional Difference Processes 5.2.6 Example: IBM Stock Prices ...
Página xi
... Simulated AR(1) Model 7.2.4 Example: IBM Stock Prices Testing Homogeneity of Variance 7.3. I Locating a Variance Change 7.3.2. Example: IBM Stock Prices 7.3.3 Extension: Multiple Variance Changes The Wavelet Covariance and Cross ...
... Simulated AR(1) Model 7.2.4 Example: IBM Stock Prices Testing Homogeneity of Variance 7.3. I Locating a Variance Change 7.3.2. Example: IBM Stock Prices 7.3.3 Extension: Multiple Variance Changes The Wavelet Covariance and Cross ...
Página xiv
... Simulated AR(2) process with a periodic component Simulated AR(2) process and its Kalman filterestimate Time varying beta estimation Example time series of sinusoids Partitioning of the time-frequency plane Sine wave with jump ...
... Simulated AR(2) process with a periodic component Simulated AR(2) process and its Kalman filterestimate Time varying beta estimation Example time series of sinusoids Partitioning of the time-frequency plane Sine wave with jump ...
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