Modeling and Forecasting DigitalCurrency Volatility with GARCH(1,1)

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dc.contributor.authorBizhigit Sagidolla
dc.contributor.authorMaral Zholaman
dc.contributor.authorMeruert Bilyalova
dc.contributor.authorAyagoz Sagidolla
dc.date.accessioned2025-10-17T07:24:43Z
dc.date.available2025-10-17T07:24:43Z
dc.date.issued2025
dc.description.abstractThe burgeoning field of digital currencies presents unique challenges for predictive modeling due totheir inherent volatility and market dynamics distinct from traditional financial assets.We study the use of the GARCH(1,1) model to characterize and forecast the conditional volatility ofdaily Bitcoin returns. Using standard OHLCV data, we estimate a parsimonious GARCH(1,1) specificationand produce one-step-ahead volatility forecasts. We discuss model assumptions, stability conditions, andpractical considerations for risk metrics (e.g., VaR). The aim is to document a transparent, reproduciblepipeline rather than to compare exhaustively against alternative models. Results illustrate how a standardGARCH(1,1) specification can provide interpretable volatility estimates for Bitcoin, serving as a transparentbaseline rather than a novel predictive breakthrough.
dc.identifier.citationBizhigit Sagidolla, Maral Zholaman, Meruert Bilyalova, and Ayagoz Sagidolla / Modeling and Forecasting DigitalCurrency Volatility with GARCH(1,1) / Journal of Emerging Technologies and Computing (JETC), Vol. 2 No. 2 / SDU University / 2025
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/2075
dc.language.isoen
dc.publisherSDU University
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectBitcoin
dc.subjectGARCH(1,1)
dc.subjectvolatility forecasting
dc.subjectData-Driven forecasting
dc.subjectrisk management
dc.titleModeling and Forecasting DigitalCurrency Volatility with GARCH(1,1)
dc.typeArticle

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