Author(s)

Lawrence Dhliwayo¹, Florance Matarise¹, Charles Chimedza²

¹Department of Statistics, University of Zimbabwe, Harare, Zimbabwe.
²School of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg, South Africa.

This paper introduces the class of seasonal fractionally integrated autoregressive moving average-generalized conditional heteroskedastisticty (SARFIMA- GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models was derived. Test statistics that are useful to examine if seasonal level shift in an SARFIMA-GARCH model is statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters was also considered.

Seasonality, Fractional Integration, Long-Memory, Level Shift, SLS-SARFIMA, SLS-GARCH, Volatility

Dhliwayo, L. , Matarise, F. and Chimedza, C. (2020) Modeling Seasonal Fractionally Integrated Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity Model with Seasonal Level Shift Intervention. Open Journal of Statistics, 10, 810-831. doi: 10.4236/ojs.2020.105047.