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Moffat, I.U. and Akpan, E.A. (2017) Identification and Modeling of Outliers in a Discrete-Time Stochastic Series. American Journal of Theoretical and Applied Statistics, 6, 191-197.
https://doi.org/10.11648/j.ajtas.20170604.14

has been cited by the following article:

• TITLE: Application of Iterative Approaches in Modeling the Efficiency of ARIMA-GARCH Processes in the Presence of Outliers

  AUTHORS: Emmanuel Alphonsus Akpan, K. E. Lasisi, Ali Adamu, Haruna Bakari Rann

  KEYWORDS: Heteroscedasticity, Kurtosis, Model Efficiency, Outliers, Unconditional Variance, Volatility

  JOURNAL NAME: Applied Mathematics, Vol.10 No.3, March 29, 2019

  ABSTRACT: The study explored both Box and Jenkins, and iterative outlier detection procedures in determining the efficiency of ARIMA-GARCH-type models in the presence of outliers using the daily closing share price returns series of four prominent banks in Nigeria (Skye (Polaris) bank, Sterling bank, Unity bank and Zenith bank) from January 3, 2006 to November 24, 2016. The series consists of 2690 observations for each bank. The data were obtained from the Nigerian Stock Exchange. Unconditional variance and kurtosis coefficient were used as criteria for measuring the efficiency of ARIMA-GARCH-type models and our findings revealed that kurtosis is a better criterion (as it is a true measure of outliers) than the unconditional variance (as it can be depleted or amplified by outliers). Specifically, the strength of this study is in showing the applicability and relevance of iterative methods in time series modeling.