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
... 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 4.2 4.3 4.4 4.5 4.6 4.7 4 DISCRETE CONTENTS.
... 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 4.2 4.3 4.4 4.5 4.6 4.7 4 DISCRETE CONTENTS.
Página xi
... Beta Estimation Univariate and Bivariate Spectrum Analysis 7.7. I Univariate Spectrum Analysis 7.7.2 Univariate Spectrum Estimation 7.7.3 Equivalent Degrees of Freedom for a Spectral Estimator 7.7.4 Bivariate Spectrum Analysis - 7.7.5 ...
... Beta Estimation Univariate and Bivariate Spectrum Analysis 7.7. I Univariate Spectrum Analysis 7.7.2 Univariate Spectrum Estimation 7.7.3 Equivalent Degrees of Freedom for a Spectral Estimator 7.7.4 Bivariate Spectrum Analysis - 7.7.5 ...
Página xiv
... beta estimation Example time series of sinusoids Partitioning of the time-frequency plane Sine wave with jump discontinuity Morlet wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency ...
... beta estimation Example time series of sinusoids Partitioning of the time-frequency plane Sine wave with jump discontinuity Morlet wavelet - Wavelets and the Gaussian probability density function Critical sampling of the time-frequency ...
Página xvii
... betas for different companies Out-of-sample MSPE of the S&P-500 call options Out-of-sample MSPE per maturity and moneyness Summary statistics for the daily exchange rates Out-of-sample predictions with technical indicators 46 82 114 115 ...
... betas for different companies Out-of-sample MSPE of the S&P-500 call options Out-of-sample MSPE per maturity and moneyness Summary statistics for the daily exchange rates Out-of-sample predictions with technical indicators 46 82 114 115 ...
Página 14
... beta estimation. Chapter 8 examines neural network filters. The focus in this chapter is confined to the function approximation and nonlinear filtering capabilities of neural network models. It starts with the elements of a typical ...
... beta estimation. Chapter 8 examines neural network filters. The focus in this chapter is confined to the function approximation and nonlinear filtering capabilities of neural network models. It starts with the elements of a typical ...
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