Volatility Strategies¶
Strategies that trade volatility patterns.
Overview¶
Volatility strategies exploit patterns in risk: - Low volatility stocks outperform - Volatility clusters and mean-reverts - Volatility risk premium exists
Strategies in This Section¶
| Strategy | Description | Complexity |
|---|---|---|
| Low Volatility | Defensive low-vol | Basic |
| Volatility Targeting | Risk-managed | Intermediate |
| Vol-Adjusted Returns | Risk-normalized | Intermediate |
| Volatility Breakout | Volatility expansion | Intermediate |
Quick Example¶
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signal low_volatility:
vol = rolling_std(ret(prices, 1), 60) * sqrt(252)
emit -zscore(vol) // Low vol = high score
portfolio main:
weights = rank(low_volatility).long_only(top=0.3)
backtest from 2015-01-01 to 2024-12-31
Key Considerations¶
Low Vol Anomaly¶
Lower risk stocks have historically delivered better risk-adjusted returns. Theories: - Leverage constraints - Lottery preferences - Benchmarking behavior
Volatility Clustering¶
High vol today predicts high vol tomorrow:
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signal vol_persistence:
vol_20 = rolling_std(ret(prices, 1), 20)
vol_60 = rolling_std(ret(prices, 1), 60)
emit vol_20 / vol_60 // Rising vol indicator
Expected Performance¶
| Metric | Typical Range |
|---|---|
| Sharpe | 0.5 - 1.0 |
| Annual Return | 4% - 8% |
| Max Drawdown | 8% - 20% |
| Beta | 0.5 - 0.8 |