Volatility prediction for the GARCH model

Troush, N. N.; Tsybulka, V. P.

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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/339997

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DC Field	Value	Language
dc.contributor.author	Troush, N. N.
dc.contributor.author	Tsybulka, V. P.
dc.date.accessioned	2026-01-13T10:14:50Z	-
dc.date.available	2026-01-13T10:14:50Z	-
dc.date.issued	2025
dc.identifier.citation	Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the XIV Intern. Conf., Minsk, Sept. 24–27, 2025 / Belarusian State Univ. ; eds.: Yu. Kharin (ed.-in-chief) [et al.]. – Minsk : BSU, 2025. – Pp. 259-262.
dc.identifier.isbn	978-985-881-830-2
dc.identifier.uri	https://elib.bsu.by/handle/123456789/339997	-
dc.description.abstract	In this paper, the focus is on analyzing the returns of financial assets using the GARCH(1,1) model and various distributions: stable, Student’s t-distribution, and skewed Student’s t-distribution. The work includes a theoretical analysis of the model, as well as practical application to the return data of Apple Inc, Gazprom PJSC, Severstal PJSC, Microsoft, and Nike. The results show that stable distribution models provide more accurate volatility forecasts in conditions of high uncertainty. The choice of model and distribution proves to be critical for the precision of financial analysis, emphasizing the need to use more complex distributions to forecast volatility in financial markets
dc.language.iso	en
dc.publisher	Minsk : BSU
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	ЭБ БГУ::ОБЩЕСТВЕННЫЕ НАУКИ::Экономика и экономические науки
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
dc.title	Volatility prediction for the GARCH model
dc.type	conference paper
Appears in Collections:	2025. Computer Data Analysis and Modeling: Stochastics and Data Science

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