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 xvii
... Summary statistics for the daily exchange rates Out-of-sample predictions with technical indicators 46 82 114 115 119 152 153 159 167 215 249 265 308 309 312 3.14 This Page Intentionally Left Blank ACKNOWLEDGN ENTS e are thankful xvii.
... Summary statistics for the daily exchange rates Out-of-sample predictions with technical indicators 46 82 114 115 119 152 153 159 167 215 249 265 308 309 312 3.14 This Page Intentionally Left Blank ACKNOWLEDGN ENTS e are thankful xvii.
Página 11
... exchange volatility. (a) Absolute returns of 20-min for DEM-USD exchange rate. (b) Wavelet smooth corresponding to physical scale of approximately one-day December 1, 1986, through May 29, 1987. Data source: Olsen & Associates. Figure ...
... exchange volatility. (a) Absolute returns of 20-min for DEM-USD exchange rate. (b) Wavelet smooth corresponding to physical scale of approximately one-day December 1, 1986, through May 29, 1987. Data source: Olsen & Associates. Figure ...
Página 12
... exchange rate returns. The individual cross-correlation functions correspond to – from bottom to top-levels of the wavelet decomposition associated with changes of 1, 2, 4, 8, 16, and 32 months. The dotted lines indicate the approximate ...
... exchange rate returns. The individual cross-correlation functions correspond to – from bottom to top-levels of the wavelet decomposition associated with changes of 1, 2, 4, 8, 16, and 32 months. The dotted lines indicate the approximate ...
Página 13
... exchange rates, multiscale causality and cointegration, and the multiscale relationship between money growth and inflation. Chapter 5 looks at the DWT applied to two types of stationary time series models: long-memory processes and ...
... exchange rates, multiscale causality and cointegration, and the multiscale relationship between money growth and inflation. Chapter 5 looks at the DWT applied to two types of stationary time series models: long-memory processes and ...
Página 14
... exchange markets and multiscale 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 ...
... exchange markets and multiscale 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 ...
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