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
... Volatility Estimation 2.4.2 The Hodrick-Prescott Filter 2.4.3 The Baxter-King (BK) Filter 2.4.4 Filters in Technical Analysis of Financial Markets 3 OPTIMUM LINEAR ESTIMATION Introduction The Wiener Filter and Estimation 3.2.1 Example ...
... Volatility Estimation 2.4.2 The Hodrick-Prescott Filter 2.4.3 The Baxter-King (BK) Filter 2.4.4 Filters in Technical Analysis of Financial Markets 3 OPTIMUM LINEAR ESTIMATION Introduction The Wiener Filter and Estimation 3.2.1 Example ...
Página xi
... Volatility 6.5.4 Outlier Testing 7 VVAVELETS FORVARIANCE-COVARIANCE ESTINATION Introduction The Wavelet Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence Intervals for the Wavelet Variance 7.2.3 Example: Simulated AR(1) ...
... Volatility 6.5.4 Outlier Testing 7 VVAVELETS FORVARIANCE-COVARIANCE ESTINATION Introduction The Wavelet Variance 7.2.1 Estimating the Wavelet Variance 7.2.2 Confidence Intervals for the Wavelet Variance 7.2.3 Example: Simulated AR(1) ...
Página xii
... Volatility Prediction Recurrent Networks 8.4.1 Output-Recurrent Model 8.4.2 Hidden-Recurrent Model 8.4.3 Output-Hidden Recurrent Model Network Selection 8.5. l Information Theoretic Criteria 8.5.2 Cross-Validation 8.5.3 Bayesian ...
... Volatility Prediction Recurrent Networks 8.4.1 Output-Recurrent Model 8.4.2 Hidden-Recurrent Model 8.4.3 Output-Hidden Recurrent Model Network Selection 8.5. l Information Theoretic Criteria 8.5.2 Cross-Validation 8.5.3 Bayesian ...
Página xiii
... volatility Scaling of foreign exchange volatility across time horizons Multiresolution analysis of foreign exchange volatility Wavelet cross-correlation between exchange rate returns DEM-USD exchange rate and its centered moving ...
... volatility Scaling of foreign exchange volatility across time horizons Multiresolution analysis of foreign exchange volatility Wavelet cross-correlation between exchange rate returns DEM-USD exchange rate and its centered moving ...
Página xiv
... volatility series Phase diagram for the OLG pricing function Multiresolution analysis of OLG price series (6 = 3.1) Multiresolution analysis of OLG price series (6 = 4) MODWT decompositions of the IBM return series Sum of squared MODWT ...
... volatility series Phase diagram for the OLG pricing function Multiresolution analysis of OLG price series (6 = 3.1) Multiresolution analysis of OLG price series (6 = 4) MODWT decompositions of the IBM return series Sum of squared MODWT ...
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