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.
|
Dentro del libro
Resultados 1-5 de 58
Página vii
... 3.3 3.4 3.5 3.6 2. Denoising Identification of Structural Breaks Scaling Aggregate Heterogeneity and Timescales Multiscale Cross-Correlation Outline 10 13 5. I 5.2 Introduction Wavelets and Long-Memory Processes 5.2. I vii CONTENTS.
... 3.3 3.4 3.5 3.6 2. Denoising Identification of Structural Breaks Scaling Aggregate Heterogeneity and Timescales Multiscale Cross-Correlation Outline 10 13 5. I 5.2 Introduction Wavelets and Long-Memory Processes 5.2. I vii CONTENTS.
Página xi
... Correlation and Cross-Correlation 7.5.1 Estimation 7.5.2 Confidence Intervals 7.5.3 Example: Monthly Foreign Exchange Rates Applications 7.6.1 Scaling Laws in FX Markets 7.6.2 Multiscale Beta Estimation Univariate and Bivariate Spectrum ...
... Correlation and Cross-Correlation 7.5.1 Estimation 7.5.2 Confidence Intervals 7.5.3 Example: Monthly Foreign Exchange Rates Applications 7.6.1 Scaling Laws in FX Markets 7.6.2 Multiscale Beta Estimation Univariate and Bivariate Spectrum ...
Página xiii
... correlation between exchange rate returns DEM-USD exchange rate and its centered moving averages DEM-USD exchange rate and its simple moving averages Filter coefficients in a simple moving average A circle with radius R Cyclical ...
... correlation between exchange rate returns DEM-USD exchange rate and its centered moving averages DEM-USD exchange rate and its simple moving averages Filter coefficients in a simple moving average A circle with radius R Cyclical ...
Página xiv
... correlation A signal and its Kalman filter estimate 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 ...
... correlation A signal and its Kalman filter estimate 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 ...
Página xvi
... correlation between the DEM-USD and JPY-USD exchange rate returns Wavelet variance for 20-min absolute returns Threshold activation function Logistic activation function Piecewise linear activation function Signum activation function ...
... correlation between the DEM-USD and JPY-USD exchange rate returns Wavelet variance for 20-min absolute returns Threshold activation function Logistic activation function Piecewise linear activation function Signum activation function ...
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