Comparative Analysis of LSTM vs. Transformer Models for Volatility Forecasting: A Theoretical Review with Applications to VIX and Cryptocurrency Markets
George Panos
Abstract
networks and Transformer models for financial volatility forecasting, with specific
applications to the CBOE Volatility Index (VIX) and cryptocurrency markets (Bitcoin,
Ethereum). Unlike existing empirical comparisons that focus solely on forecast accuracy
metrics such as RMSE or QLIKE, this study examines architectural assumptions, training
requirements, sample efficiency, interpretability, and theoretical suitability under different
market regimes. We find that LSTMs offer superior sample efficiency (500–1,000
observations) and interpretability for low-frequency (daily) volatility forecasting in
stationary regimes, while Transformers excel at capturing long-range dependencies
(beyond 200 lags) and regime shifts, particularly in high-frequency cryptocurrency data
(hourly or minute-level). However, Transformers require substantially more data (10,000–
50,000 observations) and computational resources, making them impractical for smaller
datasets. We propose a hybrid LSTM-attention framework that balances both approaches
and identify open research problems including rough volatility integration, real-time
attention mechanisms, and attention weight calibration. This review provides actionable
guidance for quantitative researchers and practitioners selecting between these
architectures for volatility forecasting tasks.