Version 1
: Received: 18 November 2018 / Approved: 22 November 2018 / Online: 22 November 2018 (14:56:57 CET)
How to cite:
Adenomon, M.O.; Emenogu, N.G.; Obinna, N.N. On the Performance of Garch Family Models in the Presence of Additive Outliers. Preprints2018, 2018110552. https://doi.org/10.20944/preprints201811.0552.v1
Adenomon, M.O.; Emenogu, N.G.; Obinna, N.N. On the Performance of Garch Family Models in the Presence of Additive Outliers. Preprints 2018, 2018110552. https://doi.org/10.20944/preprints201811.0552.v1
Adenomon, M.O.; Emenogu, N.G.; Obinna, N.N. On the Performance of Garch Family Models in the Presence of Additive Outliers. Preprints2018, 2018110552. https://doi.org/10.20944/preprints201811.0552.v1
APA Style
Adenomon, M.O., Emenogu, N.G., & Obinna, N.N. (2018). On the Performance of Garch Family Models in the Presence of Additive Outliers. Preprints. https://doi.org/10.20944/preprints201811.0552.v1
Chicago/Turabian Style
Adenomon, M.O., Ngozi G. Emenogu and Nweze Nwaze Obinna. 2018 "On the Performance of Garch Family Models in the Presence of Additive Outliers" Preprints. https://doi.org/10.20944/preprints201811.0552.v1
Abstract
It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.
Keywords
Additive Outliers, Models, Simulation, Time Series length, R Software
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
