An alternative method for out-of-sample forecast of FIEGARCH model

Rakshit, Debopam; Paul, Ranjit Kumar; Yeasin, Md.; Chesneau, Christophe

doi:10.47974/JSMS-1357

Open Access Research Article

An alternative method for out-of-sample forecast of FIEGARCH model

^*Debopam RakshitCorresponding authordebopam.rakshit@icar.gov.inThe Graduate School ICAR-Indian Agricultural Research Institute; ICAR-Indian Veterinary Research Institute IzatnagarThe Graduate School ICAR-Indian Agricultural Research InstituteNew Delhi, 110012, IndiaView full profile → , Ranjit Kumar Paulranjit.paul@icar.gov.inDivision of Statistical Genetics ICAR- Indian Agricultural Statistics Research InstituteNew Delhi, 110012, IndiaView full profile → , Md. Yeasinmd.yeasin@icar.gov.inDivision of Statistical Genetics ICAR- Indian Agricultural Statistics Research InstituteNew Delhi, 110012, IndiaView full profile → , Christophe Chesneauchristophe.chesneau@gmail.comDepartment of Mathematics University of Caen-NormandieUniversité de Caen Laboratoire de Mathématiques Nicolas Oresme Campus II, Science 3Caen, 14032, FranceView full profile →

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pp. 671–689Vol. 28Issue 4May 2025DOI: 10.47974/JSMS-1357XML

Received:: 07 Nov 2023
Published Online:: 06 May 2025
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1357
Pages:: 671–689

Abstract

Volatility is an inherent characteristic of a time series (TS). It is said to be asymmetric when negative and positive shocks of equal scale have different effects. If the volatility of one epoch is influenced by its distant counterpart, then volatility has a long memory structure. Nonlinear variance models such as the FIEGARCH model are used to deal with asymmetric volatility and long memory simultaneously. This article attempts to derive the direct formulae regarding the ‘out-of-sample’ forecast and the error variances associated with these forecasts for the ARMA(1, 0)- FIEGARCH(1, d, 1) model. Four real-life TS data are used for empirical purposes. It is observed that each TS has a significant long memory in volatility and its volatility is asymmetric.

Keywords

Asymmetry GARCH Long-term persistence Time series Volatility

Subject Classifications

91B8462M10

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