<p>The Extreme volatility of BTC challenges conventional financial modeling since it is sensitive to sentiment fluctuations, regulatory shocks, and structural market changes. GARCH models often fail to capture the complexities of Bitcoin, including nonlinearity, structural breaks, fat tails, and outliers. Building on current literature that treats either breaks or outliers, this study contributes a hybrid GARCH-type framework that incorporates structural breaks, trend breaks, and outliers through indicator saturation and winsorization strategies. Using 3143 daily data points from 2014 to 2023, this study evaluated 72 GARCH-type models, including 12 benchmark and 60 hybrid models. The models are assessed for both in-sample and out-of-sample performance. Results show that hybrid models significantly outperform benchmarks. Incorporating winsorized trend breaks under heavy-tailed distributions effectively reduces persistence and half-life, improving volatility modeling in cryptocurrency markets, which is followed by the consideration of structural breaks and outliers. These findings confirm the literature's emphasis on structural changes as major drivers of volatility, providing investors and policymakers with better tools to manage cryptocurrency risks.</p>