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 61
Página 52
... error defined by the difference between the signal x and the estimate £N via €N = x - £N. If the expected value of the estimation error is zero, the corresponding estimator is said to be an unbiased estimator. In this example, E(ex) = E ...
... error defined by the difference between the signal x and the estimate £N via €N = x - £N. If the expected value of the estimation error is zero, the corresponding estimator is said to be an unbiased estimator. In this example, E(ex) = E ...
Página 53
... error, other criteria such as the expected absolute estimation error, E(leND = E ( x – £NI), or the expected squared estimation error, E(e)= E|a-RN)']. may be used to evaluate an estimator. Among these, the expected squared estimation ...
... error, other criteria such as the expected absolute estimation error, E(leND = E ( x – £NI), or the expected squared estimation error, E(e)= E|a-RN)']. may be used to evaluate an estimator. Among these, the expected squared estimation ...
Página 55
... error eN in estimating the signal xN is not correlated with any observation y, or with any linear combination of observations. Notice that by expanding Equation 3.8 over i, E(yNyN-j)w' + E(yN-1yN-j)w' + · · · + E(y1 yN-j)w'_1 = E(xNyN-j) ...
... error eN in estimating the signal xN is not correlated with any observation y, or with any linear combination of observations. Notice that by expanding Equation 3.8 over i, E(yNyN-j)w' + E(yN-1yN-j)w' + · · · + E(y1 yN-j)w'_1 = E(xNyN-j) ...
Página 57
... error, N-1 3 N = XD w£yN—i + eN, i=0 and since E[eN (XX'o' w£yN-)] = 0, by virtue of the orthogonality condition in Equation 3.9, it follows that N–1 2 E(xã) = E(e.) + E ($ *) • (3.14) i=0 By rearranging the previous equation, we have M ...
... error, N-1 3 N = XD w£yN—i + eN, i=0 and since E[eN (XX'o' w£yN-)] = 0, by virtue of the orthogonality condition in Equation 3.9, it follows that N–1 2 E(xã) = E(e.) + E ($ *) • (3.14) i=0 By rearranging the previous equation, we have M ...
Página 58
... error). We now present an example of a linear estimation problem. 3.2.1 Example: Real Wage Estimation The decision to work is usually assumed to be a function of the real wage (i.e., the purchasing power of the nominal wage). Suppose ...
... error). We now present an example of a linear estimation problem. 3.2.1 Example: Real Wage Estimation The decision to work is usually assumed to be a function of the real wage (i.e., the purchasing power of the nominal wage). Suppose ...
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