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
... Response (IIR) Filters 2.2.2 Noncausal Finite Impulse Response (FIR) Filters 2.2.3 Causal FIR Filters Filters in the Frequency Domain 2.3.1 Frequency Response 2.3.2. Low-Pass and High-Pass Filters Filters in Practice 2.4.1 The EWMA and ...
... Response (IIR) Filters 2.2.2 Noncausal Finite Impulse Response (FIR) Filters 2.2.3 Causal FIR Filters Filters in the Frequency Domain 2.3.1 Frequency Response 2.3.2. Low-Pass and High-Pass Filters Filters in Practice 2.4.1 The EWMA and ...
Página 13
... response (FIR) and infinite impulse response (IIR) filters are introduced with simple examples. Filters in the frequency domain are also reviewed, and some frequency-domain concepts – such as a high-pass filter, low-pass filter, band ...
... response (FIR) and infinite impulse response (IIR) filters are introduced with simple examples. Filters in the frequency domain are also reviewed, and some frequency-domain concepts – such as a high-pass filter, low-pass filter, band ...
Página 17
... response of a filter is finite, it is called a finite impulse response filter, or an FIR filter. On the other hand, if the impulse response of a filter is not finite, the corresponding filter is called an infinite impulse response ...
... response of a filter is finite, it is called a finite impulse response filter, or an FIR filter. On the other hand, if the impulse response of a filter is not finite, the corresponding filter is called an infinite impulse response ...
Página 18
... Response (IIR) Filters Linear filters may also be viewed as a special case of constant coefficient linear difference equations, L M y =XLay:- +X w.x-i, (2.5) i=1 i=0 where L lagged values of output y, and M lagged values of input xt, as ...
... Response (IIR) Filters Linear filters may also be viewed as a special case of constant coefficient linear difference equations, L M y =XLay:- +X w.x-i, (2.5) i=1 i=0 where L lagged values of output y, and M lagged values of input xt, as ...
Página 19
... response with infinite duration. Therefore, it is an infinite impulse response (IIR) filter. In fact, any filter in the form of a difference equation would have an impulse response with infinite duration. If the difference equation is ...
... response with infinite duration. Therefore, it is an infinite impulse response (IIR) filter. In fact, any filter in the form of a difference equation would have an impulse response with infinite duration. If the difference equation is ...
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