Forecasting Bitcoin Volatility Spikes with On-Chain and Whale Data

Summary

The paper investigates whether CryptoQuant signals, including on-chain, exchange, and miner data, and whale-alert posts can help forecast next-day Bitcoin volatility, especially extreme spikes. It proposes a Synthesizer Transformer forecasting model and compares its performance with existing approaches. The authors report that their model performs better on extreme-volatility prediction when using these data sources, though the description gives no sample period, asset-market details, or numerical comparison results.

The study uses feature attribution analysis to examine which inputs matter and backtests predictions through several baseline trading strategies. It reports reduced drawdown alongside steady profits, but provides no specific measures, strategy rules, or transaction-cost details in the summary. These results therefore indicate a potential application for risk management and trading, while the available account does not establish robustness across market regimes or out-of-sample settings.

Key ideas

• The target is next-day Bitcoin volatility, with particular attention to extreme spikes.
• Inputs include CryptoQuant data and whale-alert posts.
• A Synthesizer Transformer is reported to outperform comparison models on extreme-volatility forecasts.
• Feature attribution is used to inspect influential inputs.
• Backtests report lower drawdown and steady profits, without details on metrics or robustness.

Tags

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


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.