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Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models. (arXiv:2211.08281v1 [q-fin.TR])
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv bitcoin data forecasting transactions transformer transformer models