Khudhayr A. Rashedi1,2,*, Mohd Tahir Ismail1, Abdeslam Serroukh3, S. Al wadi4
CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3835-3847, 2022, DOI:10.32604/cmc.2022.026476
Abstract We introduce a new wavelet based procedure for detecting outliers in financial discrete time series. The procedure focuses on the analysis of residuals obtained from a model fit, and applied to the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) like model, but not limited to these models. We apply the Maximal-Overlap Discrete Wavelet Transform (MODWT) to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers. Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform (DWT). The series sample size does not need to be a power of 2 and the… More >
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