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 18
... standard techniques." We will present only a simple example of linear difference equations to relate them with filtering. Consider the following first-order difference equation, yt = ayt–1 + xt, (2.6) where the output y, cannot be ...
... standard techniques." We will present only a simple example of linear difference equations to relate them with filtering. Consider the following first-order difference equation, yt = ayt–1 + xt, (2.6) where the output y, cannot be ...
Página 40
... standard. deviation, a potential drop in the exchange rate in one day at a given probability might be calculated from the standard normal distribution. For example, if the daily standard deviation is calculated as 0.5%, VaR at 5 ...
... standard. deviation, a potential drop in the exchange rate in one day at a given probability might be calculated from the standard normal distribution. For example, if the daily standard deviation is calculated as 0.5%, VaR at 5 ...
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... standard deviation, N is filter length (averaging period), ri is daily log price difference (return), and r is period average and X is the decay factor (0 < x < 1). The decay factor determines the relative importance of recent ...
... standard deviation, N is filter length (averaging period), ri is daily log price difference (return), and r is period average and X is the decay factor (0 < x < 1). The decay factor determines the relative importance of recent ...
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... standard deviation. The decay factor in EWMA estimate is 0.94 (X = 0.94) as suggested by RiskMetrics. Notice that the EWMA estimate of volatility captures the sudden jump in volatility around I 10th day better relative to the simple ...
... standard deviation. The decay factor in EWMA estimate is 0.94 (X = 0.94) as suggested by RiskMetrics. Notice that the EWMA estimate of volatility captures the sudden jump in volatility around I 10th day better relative to the simple ...
Página 43
... standard deviation. For applications of the Holt-Winters smoothing method in economics, see Chatfield and Yar (1991) and Grubb and Mason (2001). 2.4.2 The Hodrick-Prescott Filter The Hodrick and Prescott (HP) filter. 2.4 FILTERS IN ...
... standard deviation. For applications of the Holt-Winters smoothing method in economics, see Chatfield and Yar (1991) and Grubb and Mason (2001). 2.4.2 The Hodrick-Prescott Filter The Hodrick and Prescott (HP) filter. 2.4 FILTERS IN ...
Contenido
1 | |
15 | |
CHAPTER 3 OPTIMUM LINEAR ESTIMATION | 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