This paper presents a comparative theoretical analysis of Long Short-Term Memory (LSTM) networks and Transformer models for financial volatility forecasting, with specific applications to the CBOE Volatility Index (VIX) and cryptocurrency markets (Bitcoin and Ethereum). Unlike existing empirical studies that focus solely on forecast accuracy metrics such as RMSE or QLIKE, this research examines architectural assumptions, training requirements, sample efficiency, interpretability, and theoretical suitability under different market regimes. The study systematically compares both models across six critical dimensions: long-range dependency capture, regime shift detection, noise robustness, training parallelism, online updating feasibility, and interpretability. Theoretical predictions are derived from architectural properties and then compared against empirical findings from the literature (2018–2024). Key findings include: (1) LSTMs require only 500–1,000 observations to perform well, while Transformers need 10,000–50,000; (2) Transformers excel at capturing frequent regime shifts in high-frequency cryptocurrency data; (3) LSTMs are more robust to measurement noise and are strongly preferred for real-time trading applications; (4) Hybrid LSTM-attention models often match or exceed pure Transformer performance with fewer parameters. The paper concludes with a practical decision matrix enabling practitioners to select the optimal model based on data frequency, sample size, market characteristics, and computational constraints. Open research problems are identified, including rough volatility integration, online attention mechanisms, and attention weight calibration. This review provides actionable guidance for quantitative researchers, financial engineers, and risk managers working with volatility forecasting models.
George panos (Thu,) studied this question.