Appraisal of excess Kurtosis through outlier-modified GARCH-type models. Issue 4 (3rd April 2023)

• Record Type: Journal Article
• Authors: Alphonsus Akpan, Emmanuel; Lasisi, Kazeem Etitayo; Moffat, Imoh Udo; Abasiekwere, Ubon Akpan
• Is Part Of: Communications in statistics. Volume 52:Issue 4(2023)
• Page Start: 1523
• Page End: 1537
• Publication Date: 2023-04-03
• Subjects: Heteroscedasticity -- Outliers -- Serial correlation -- Time Series -- Volatility; 37M10 -- 62M10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2021.1887225
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: The aim of this paper is to appraise if there is any improvement subtracting the effects of outliers from existing heteroscedastic models and whether this improvement makes difference with the existing models in achieving efficiency in capturing excess kurtosis in the returns series. The study employed both existing and outlier modified autoregressive conditional heteroscedastic (ARCH), generalized autoregressive conditional heteroscedastic (GARCH), exponential GARCH (EGARCH), Glosten, Jagnnathan and Runkle GARCH (GJR-GARCH) models with respect to normal and student-t distributions to assess the portion of excess kurtosis of the returns series expressed compare to the theoretical value of kurtosis. The data applied were the share prices of Union bank of Nigeria and Unity bank from January 3, 2006 to November 24, 2016, comprising 2690 observations and were obtained from Nigerian Stock Exchange. The results obtained revealed that the Outlier Modified GARCH-type models chosen were adequate and sufficiently reducing the value of excess kurtosis in close proximity to the theoretical value. Therefore, the modification of existing GARCH-type models by subtracting the effects of outliers seems to show a substantive improvement in the portion of excess kurtosis captured and thus proves that the Outlier Modified GARCH-type models make difference with the existing ones.