An Exploration of The Volatility Clustering Present in Bitcoin’s Price Data, Comparing The GARCH, EGARCH And GJR-GARCH Models.

This study looks at the dynamics of volatility clustering in Bitcoin’s price data by comparing the performance of three econometric models namely, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential GARCH (EGARCH), and Glosten Jagannathan Runkle GARCH (GJR-GARCH) models. Using daily closing price data, the analysis explores the sensitivity of Bitcoin’s conditional variance to market shocks and the persistence of these effects over time. To achieve this, the data is cleaned and the series is differenced to achieve stationarity, after which the conditional mean and variance equations are estimated to model time-varying volatility. Model adequacy and comparative performance are assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and log-likelihood values, while out-of-sample forecast accuracy is evaluated through Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) metrics. The study examines the real-world relevance of volatility modeling by integrating the best-performing model into volatility-adjusted trading strategies and comparing their risk-adjusted returns measured by the Sharpe ratio with those of the conventional buy-and-hold strategy. The empirical results obtained confirm the presence of significant volatility clustering in Bitcoin’s price and indicate that the EGARCH (3,2) model most effectively captures volatility responses to shocks in the market. The EGARCH (3,2) model also demonstrates higher forecasting performance relative to the others. When implemented within a volatility-based position sizing framework, it yields higher risk-adjusted returns than the buy and hold strategy. These findings show the value of advanced conditional heteroskedasticity models in enhancing predictive accuracy and informing more efficient cryptocurrency trading and risk management strategies.