Modelling and forecasting cryptocurrency volatility is essential due to the inherently volatile and speculative nature of digital asset markets. Accurate volatility predictions enable traders and investors to make informed decisions, optimize portfolio strategies, and mitigate risks in a highly uncertain environment. This study examines the volatility dynamics of large-cap and mid-cap cryptocurrencies through high-frequency data analysis. Cryptocurrencies exhibit unique market behaviours characterised by complex short-, medium-, and long-term volatility patterns, which require sophisticated modelling techniques for accurate forecasting. Among the methods explored, the Heterogeneous Autoregressive (HAR) model stands out for its ability to effectively capture multi-scale dependencies, making it particularly suitable for modelling the complex volatility trends observed in these digital assets. By assessing both in-sample and out-of-sample performance, this study points out the importance of employing multi-scale approaches to improve predictive accuracy. The findings have significant implications for risk management and trading strategies, as accurate volatility forecasting is crucial in highly volatile cryptocurrency markets. The HAR model’s capacity to integrate multiple time horizons allows for a more comprehensive understanding of market dynamics, providing practical insights for financial decision-making. This research advances the broader understanding of cryptocurrency volatility and provides a foundation for future studies to explore understudied modelling approaches to the growing complexities of digital asset markets.