№ 2 (2) 2025

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Open Access
Modeling and Forecasting DigitalCurrency Volatility with GARCH(1,1)
(SDU University, 2025) Bizhigit Sagidolla; Maral Zholaman; Meruert Bilyalova; Ayagoz Sagidolla
The 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.