Software issues in wavelet analysis of financial data - Numerical ...

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Identifying Seasonality Apply SDWT to data Wavelet detail coefficients at a given level capture a particular rangeof frequencies Identify detail coefficients carrying seasonal periodicity Filter out seasonal effectse.g. Gencay et al. (2001) for application to FX returnsResults Matter. Trust NAG.18
Self-similarityScalogram represents energy of series in the wavelet coefficientse.g. Jamdee and Los (2004) use Morlet wavelet CWT for analysis of interestrate series and calculation of Hurst exponentPredictionApply backward looking wavelet MRA to time seriesUse wavelet coefficients as input to a neural network model forpredictione.g. Renaud et al. (2004)Results Matter. Trust NAG.19
Page 1 and 2: Numerical Algorithms GroupMathemati
Page 4 and 5: Wavelet Transformsx(t),Decompose ti
Page 6 and 7: Wavelet Functions1Morlet (k=5)0.2Da
Page 9 and 10: Stationary DWT (SDWT)Note: DWT is N
Page 11 and 12: Choosing a Wavelet Method CWTContin
Page 13 and 14: CWT (2) Find common normalisation f
Page 15 and 16: Implementation and algorithms (2) D
Page 17: De-NoisingTransform data into wavel
Page 21: Summary1) Wavelet transforms provid

Software issues in wavelet analysis of financial data - Numerical ...

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