An Introduction to Wavelets and Other Filtering Methods in Finance and EconomicsAn 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 multiresolution 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 15 de 58
Página 4
The random variables et and vst are uncorrelated Gaussian disturbance terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR(1) process in Equation 1.1 with ...
The random variables et and vst are uncorrelated Gaussian disturbance terms with mean zero and unit variance. Figure 1.2 presents the autocorrelation functions (ACFs) from a length N = 1000 simulated AR(1) process in Equation 1.1 with ...
Página 7
... random variables (required by CUSUM procedures), nor can it be effectively modeled by an ARMA process with few parameters. The null hypothesis of constant variance is rejected for the first three scales of the wavelet transform.
... random variables (required by CUSUM procedures), nor can it be effectively modeled by an ARMA process with few parameters. The null hypothesis of constant variance is rejected for the first three scales of the wavelet transform.
Página 13
Seasonal longmemory time series models are fit to Mexican money supply, Japanese gross national product, and several U.S. economic variables. Chapter 6 uses the wavelet transform to recover a “signal” from noisy observations, ...
Seasonal longmemory time series models are fit to Mexican money supply, Japanese gross national product, and several U.S. economic variables. Chapter 6 uses the wavelet transform to recover a “signal” from noisy observations, ...
Página 15
INTRODUCTION The inherent interaction of a particular variable with the underlying environment may produce complicated features. Filtering methods deal with the identification and extraction of certain features (e.g., trends, ...
INTRODUCTION The inherent interaction of a particular variable with the underlying environment may produce complicated features. Filtering methods deal with the identification and extraction of certain features (e.g., trends, ...
Página 31
... consider the following time series: cos(#)+cos(#)+ (2.14) £r = COS   S   € • t 12 20 t = cos (27tfit) + cos (27tfot) + et, (2.15) where e, is a normally distributed random variable with zero mean and unit variance.
... consider the following time series: cos(#)+cos(#)+ (2.14) £r = COS   S   € • t 12 20 t = cos (27tfit) + cos (27tfot) + et, (2.15) where e, is a normally distributed random variable with zero mean and unit variance.
Comentarios de la gente  Escribir un comentario
No encontramos ningún comentario en los lugares habituales.
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 beta calculated components computed correlation covariance cycle decomposition defined determined difference discrete distribution dynamics Equation error estimator example feedforward network Figure Fourier transform frequency function gain function Gaussian given Haar hidden units increases indicate input interval Kalman filter 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 values variables variance vector volatility wavelet coefficients wavelet details wavelet filter wavelet scale wavelet transform wavelet variance weights zero