Highlights
- Machine Learning Superiority: ML models (Random Forest, LSTM) outperform traditional volatility models like GARCH.
- Hyper-Parameter Optimization: Genetic Algorithm and Artificial Bee Colony methods enhance LSTM performance.
- Determinant Analysis: Internal factors (e.g., lagged volatility) dominate volatility forecasts.
- Multi-Cryptocurrency Advantage: Models trained on multiple cryptocurrencies yield better accuracy than single-currency models.
Abstract
Cryptocurrency markets are highly volatile, demanding precise volatility forecasting to mitigate risks. This study leverages machine learning (ML) techniques—including Random Forest and Long Short-Term Memory (LSTM) networks—to predict volatility using:
- Internal determinants: Historical volatility, trading data.
- External determinants: Technology, financial, and policy uncertainty indices.
Key findings:
- ML models surpass conventional GARCH models in accuracy.
- Optimizing LSTM hyper-parameters with Genetic Algorithm and Artificial Bee Colony boosts forecast performance.
- SHAP analysis reveals internal determinants as primary volatility drivers.
- Multi-cryptocurrency-trained models capture market dynamics more effectively than single-currency models.
Keywords
- Time-series forecasting
- Cryptocurrency volatility
- Machine learning (Random Forest, LSTM)
- Deep learning optimization
- Market determinants
FAQs
1. Why do ML models outperform traditional volatility models like GARCH?
ML algorithms (e.g., LSTM) capture non-linear patterns and complex dependencies in volatility data, which rigid parametric models like GARCH often miss.
2. How do internal determinants influence cryptocurrency volatility?
Internal factors—such as past volatility and trading volume—reflect market sentiment and liquidity, directly impacting short-term price fluctuations.
3. What are the benefits of multi-cryptocurrency training?
Diverse datasets improve model generalizability by exposing it to varied market behaviors, reducing overfitting to a single asset’s noise.
4. Can these models be applied to other financial markets?
👉 Explore how ML adapts to traditional markets with similar volatility structures, like equities or commodities.
5. What’s the role of hyper-parameter optimization in LSTM?
Tuning parameters (e.g., learning rate, layer size) refines the model’s ability to learn sequential patterns, minimizing prediction errors.
6. How do external determinants like policy uncertainty affect forecasts?
Macro-level shocks (e.g., regulatory changes) indirectly alter investor behavior, creating volatility cascades detectable via ML feature importance.
Key Takeaways
- Actionable Insights: Combine internal/external determinants for robust volatility forecasts.