Improving Crypto Volatility Forecasts with Sentiment Data

Accurately forecasting volatility is essential in portfolio and risk management. Typically, volatility forecasting is performed using econometric models such as GARCH and ARIMA. These methods can also be applied to cryptocurrencies. However, a unique feature of cryptocurrencies is their higher susceptibility to market sentiment. We have discussed their sensitivity to sentiment in a previous post.

Reference [1] proposed combining traditional econometric models with sentiment analysis to forecast volatility. Specifically, it utilized RoBERTa to analyze social media and news sentiment, then developed three hybrid models for volatility forecasting. These models are,

• Sentiment-Augmented ARIMA: integrated SNR-filtered sentiment as an external variable, enabling forecasts to adjust dynamically to shifts in market mood.
• Attention-LSTM: combined past log-returns and sentiment vectors in an LSTM with self-attention to focus on periods when sentiment peaks were most predictive.
• Transformer Fusion: used multi-head self-attention on interleaved price and sentiment embeddings to capture long-range dependencies and cross-modal interactions without recurrence.

The authors pointed out,

This study demonstrates the significant benefits of integrating high‑quality sentiment signals with advanced time series models to forecast short‑term Bitcoin volatility. By introducing a robust signal‑to‑noise filtration mechanism, we were able to isolate meaningful market sentiment from the ubiquitous noise of social media chatter and news feeds. Our empirical evaluation, spanning classical ARIMA, standalone LSTM, attention‑based recurrent architectures, and a transformer‑based fusion model, clearly shows that hybrid designs outperform single‑modality approaches across error and directional accuracy metrics. In particular, the Transformer Fusion model achieved the lowest forecasting errors and highest directional reliability, while the Attention‑LSTM provided a compelling balance between performance and interpretability for real‑time applications. Beyond raw predictive gains, this framework offers actionable insights into the temporal dynamics of sentiment and volatility. Cross‑correlation analyses confirmed that filtered sentiment leads to volatility spikes by several intervals, validating its role as an early warning indicator. Moreover, the attention weights and SHAP‑inspired interpretability afford practitioners visibility into which historical windows and sentiment bursts drive model forecasts, addressing the “black‑box” concerns often associated with deep learning in financial contexts.

In short, incorporating sentiment into econometric models improves their predictive power. An interesting finding of the research is the volatility clustering of cryptocurrencies, where Bitcoin log-returns show that high-volatility periods tend to follow high-volatility periods, and calm periods follow calm periods, supporting the use of recent volatility in prediction models.

Let us know what you think in the comments below or in the discussion forum.

References

[1] MK Rahman, A Raheem, Crypto Noise vs. Signal: A Hybrid Sentiment-Time Series Framework for Predicting Short-Term Bitcoin Volatility, Global Knowledge Academy, Volume-VI, Issue-III (2025)

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