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

TL;DR

This paper introduces a Synthesizer Transformer model that leverages CryptoQuant data and whale-alert tweets to accurately forecast extreme Bitcoin volatility spikes, outperforming existing models and aiding risk management.

Contribution

The paper presents a novel deep learning Synthesizer Transformer approach that effectively predicts Bitcoin's extreme volatility spikes using on-chain analytics and social media data.

Findings

The model outperforms state-of-the-art volatility forecasting methods.

Feature importance analysis highlights key data sources influencing predictions.

Backtested trading strategies reduce drawdown while maintaining profits.

Abstract

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.