Introduction
Cryptocurrencies have emerged as high-volatility investment assets, presenting unique challenges for price prediction. This study develops advanced models to predict log-return prices of major cryptocurrencies (Bitcoin, Ethereum, Binance Coin) by incorporating volatility features derived from ARCH/GARCH models alongside traditional price data.
Key Innovations
- Volatility Integration: Combines closing prices with ARCH/GARCH-derived volatility metrics
- Feature Selection: Uses Gini impurity to identify the most influential predictors for each cryptocurrency
- Model Comparison: Benchmarks ARIMA against neural networks (RNN/LSTM/GRU)
- Practical Application: Provides actionable insights for cryptocurrency investors managing high-risk portfolios
Methodology
Data Collection & Preprocessing
- Sources: Daily closing prices from Binance API (May 2018-May 2022)
- Normalization: Min-max scaling applied to 11 cryptocurrencies' log-returns
- Stationarity: Verified through KPSS testing (all p-values > 0.05)
Feature Engineering
| Feature Type | Description | Count |
|---|---|---|
| Closing Prices | Log-return converted daily prices | 11 |
| ARCH(1) Volatility | Short-term volatility metrics | 11 |
| GARCH(1,1) | Generalized volatility metrics | 11 |
Prediction Models
- Traditional: ARIMA(2,1,0) with 2-time leg structure
Neural Networks:
- 6 architectures tested (RNN/LSTM/GRU)
- Optimal: Architecture 6 (minimal layers, lowest computation)
Key Findings
Performance Metrics (Validation Data)
| Cryptocurrency | Best Model | MAE | RMSE |
|---|---|---|---|
| Bitcoin | RNN Arch 5 | 0.0374 | 0.0491 |
| Ethereum | GRU Arch 6 | 0.0393 | 0.0486 |
| Binance Coin | RNN Arch 6 | 0.0251 | 0.0355 |
- Neural networks outperformed ARIMA by 11-15% across all metrics
- GRU showed particular strength in handling Ethereum's volatility
Feature Importance
Top 3 predictors per cryptocurrency:
- Bitcoin: BTC-ARCH, ETH-Close, LTC-Close
- Ethereum: ETH-ARCH, LTC-Close, NEO-Close
- Binance Coin: BNB-ARCH, ETH-Close, ADA-Close
Practical Applications
👉 Discover real-time cryptocurrency analytics tools to implement these models
👉 Explore volatility prediction APIs for portfolio management
Limitations & Future Research
- Scope: Limited to 11 high-cap cryptocurrencies
- Opportunity: Expand with macroeconomic factors (stock markets, bonds)
- Technical: Test transformer-based models for long-sequence forecasting
Conclusion
This study demonstrates that neural network models incorporating volatility features significantly improve log-return price prediction accuracy versus traditional methods. The ARCH/GARCH-enhanced approach provides investors with robust tools for navigating cryptocurrency markets' inherent volatility.
FAQ Section
Q: Why use log-return prices instead of raw prices?
A: Log-returns normalize volatility and make trends more statistically tractable for time-series analysis.
Q: How frequently should models be retrained?
A: Weekly retraining is recommended given cryptocurrency markets' rapid evolution.
Q: Can these models predict extreme price swings?
A: While effective for normal volatility, black swan events remain challenging to predict accurately.
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