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The cryptocurrency market is highly volatile compared to traditionalfinancial markets. Hence, forecasting its volatility is crucial for riskmanagement. In this paper, we investigate CryptoQuant data (e.g. on-chainanalytics, exchange and miner data) and whale-alert tweets, and explore theirrelationship to Bitcoin's next-day volatility, with a focus on extremevolatility spikes. We propose a deep learning Synthesizer Transformer model forforecasting volatility. Our results show that the model outperforms existingstate-of-the-art models when forecasting extreme volatility spikes for Bitcoinusing CryptoQuant data as well as whale-alert tweets. We analysed our modelwith the Captum XAI library to investigate which features are most important.We also backtested our prediction results with different baseline tradingstrategies and the results show that we are able to minimize drawdown whilekeeping steady profits. Our findings underscore that the proposed method is auseful tool for forecasting extreme volatility movements in the Bitcoin market.

Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models