  The cryptocurrency market is highly volatile compared to traditional
financial markets. Hence, forecasting its volatility is crucial for risk
management. In this paper, we investigate CryptoQuant data (e.g. on-chain
analytics, exchange and miner data) and whale-alert tweets, and explore their
relationship to Bitcoin's next-day volatility, with a focus on extreme
volatility spikes. We propose a deep learning Synthesizer Transformer model for
forecasting volatility. Our results show that the model outperforms existing
state-of-the-art models when forecasting extreme volatility spikes for Bitcoin
using CryptoQuant data as well as whale-alert tweets. We analysed our model
with the Captum XAI library to investigate which features are most important.
We also backtested our prediction results with different baseline trading
strategies and the results show that we are able to minimize drawdown while
keeping steady profits. Our findings underscore that the proposed method is a
useful tool for forecasting extreme volatility movements in the Bitcoin market.
