Modeling and Forecasting Digital Currency Volatility with GARCH(1,1)

Article

Abstract

The burgeoning field of digital currencies presents unique challenges for predictive modeling due to their 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 of daily Bitcoin returns. Using standard OHLCV data, we estimate a parsimonious GARCH(1,1) specification and produce one-step-ahead volatility forecasts. We discuss model assumptions, stability conditions, and practical considerations for risk metrics (e.g., VaR). The aim is to document a transparent, reproducible pipeline rather than to compare exhaustively against alternative models. Results illustrate how a standard GARCH(1,1) specification can provide interpretable volatility estimates for Bitcoin, serving as a transparent baseline rather than a novel predictive breakthrough.