volatility-forecasting

ML Volatility Forecasting

Enterprise-grade volatility forecasting system with GARCH family models, realized volatility estimators, ML-based prediction, and real-time risk metrics.

Overview

Production-ready volatility forecasting framework combining classical econometric models (GARCH family) with modern machine learning approaches for accurate volatility prediction and risk management.

Key Capabilities

• GARCH Family Models — GARCH(1,1), EGARCH, GJR-GARCH, FIGARCH, DCC-GARCH with automatic model selection
• Realized Volatility — RV, Bipower Variation, Realized Kernels, Realized GARCH
• Implied Volatility — Black-Scholes IV extraction, volatility surface interpolation
• ML Models — LSTM networks, HAR-RV, Random Forest for volatility prediction
• Risk Metrics — VaR, CVaR, volatility cones, tail risk analysis
• Real-Time API — FastAPI with WebSocket streaming for live volatility updates
• Trading Integration — Position sizing, volatility-adjusted stop losses

Architecture

src/
├── models/
│   ├── garch_models.py          # GARCH, EGARCH, GJR-GARCH, FIGARCH, DCC-GARCH
│   └── ml_volatility.py         # LSTM, HAR-RV, Random Forest models
├── estimators/
│   ├── realized_volatility.py   # RV, BPV, Realized Kernels
│   └── implied_volatility.py    # Black-Scholes IV, volatility surface
├── core/
│   └── volatility_system.py     # Unified forecasting system
├── api/
│   └── volatility_api.py        # FastAPI + WebSocket endpoints
├── utils/
│   ├── risk_metrics.py          # VaR, CVaR, volatility cones
│   └── trading_integration.py   # Position sizing, risk management
└── validation/
    └── volatility_validator.py  # Model validation & backtesting

GARCH Models

The framework implements the complete GARCH family with automatic model selection:

Model	Use Case	Key Feature
GARCH(1,1)	Baseline volatility clustering	Standard conditional heteroskedasticity
EGARCH	Asymmetric volatility	Leverage effect modeling
GJR-GARCH	News impact asymmetry	Threshold effects for positive/negative shocks
FIGARCH	Long memory	Fractionally integrated for persistent volatility
DCC-GARCH	Multi-asset	Dynamic conditional correlations

from ml_volatility_forecasting import GARCHModelSelector

selector = GARCHModelSelector()
best_model = selector.select_best_model(
    returns_data,
    candidates=["garch", "egarch", "gjr-garch", "figarch"],
    criterion="bic"  # AIC, BIC, or log-likelihood
)

forecast = best_model.forecast(horizon=10)

Risk Metrics

Built-in risk analysis tools:

from ml_volatility_forecasting import RiskMetrics

risk = RiskMetrics(returns_data)

# Value at Risk
var_95 = risk.calculate_var(confidence=0.95, method="historical")
var_99 = risk.calculate_var(confidence=0.99, method="parametric")

# Conditional VaR (Expected Shortfall)
cvar = risk.calculate_cvar(confidence=0.95)

# Volatility cones
cones = risk.volatility_cones(windows=[7, 14, 30, 60, 90])

ML Volatility Models

Combine classical and ML approaches:

from ml_volatility_forecasting import LSTMVolatilityModel, HARRVModel

# LSTM for sequence modeling
lstm = LSTMVolatilityModel(
    lookback=60,
    hidden_size=128,
    num_layers=2
)
lstm.fit(training_data)
prediction = lstm.predict(horizon=5)

# HAR-RV (Heterogeneous Autoregressive Realized Volatility)
har = HARRVModel()
har.fit(realized_vol_data)

Real-Time API

FastAPI server with WebSocket streaming:

# Start the API server
# uvicorn ml_volatility_forecasting.api.volatility_api:app --host 0.0.0.0 --port 8000

# REST endpoints:
# GET  /api/v1/forecast/{symbol}?horizon=10&model=egarch
# GET  /api/v1/risk-metrics/{symbol}
# GET  /api/v1/volatility-surface/{symbol}
# WS   /ws/volatility-stream

Installation

pip install -e .

# With GPU support
pip install -e ".[gpu]"

# With quantitative finance tools
pip install -e ".[quant]"

# Development
pip install -e ".[dev]"

Configuration

Set environment variables for external services:

export DATABASE_URL="postgresql://localhost/volatility"
export REDIS_URL="redis://localhost:6379"

Tech Stack

• Econometrics: arch, statsmodels, pykalman
• ML: PyTorch, TensorFlow, scikit-learn
• API: FastAPI, WebSockets, uvicorn
• Data: pandas, NumPy, SciPy, Numba (JIT)
• Storage: PostgreSQL, Redis, ClickHouse
• Monitoring: Prometheus, structlog

License

MIT License — see LICENSE for details.

Support

For questions and support, please open an issue on GitHub.