If you are building a volatility-forecasting model, one recurring design decision is whether to prioritize option-implied volatility or historical volatility.
The short answer: implied volatility (IV) is usually the stronger standalone predictor of future realized volatility (RV), but it works best when combined with historical or GARCH-based estimates.
The two inputs Implied volatility comes from option prices. It represents the market’s expectation of future volatility and can update quickly as new information arrives.
Historical volatility is computed from past returns. It is easy to calculate and useful because volatility tends to cluster, but it is backward-looking.
What the evidence says
Studies across futures markets show IV often outperforms HV, and HV may add limited value once IV is already in the model. Research on option-implied distributions reaches a similar conclusion: IV is generally better than historical time-series estimates, though it is biased.
That bias matters. IV frequently exceeds subsequent RV because option prices include a volatility risk premium.
A practical modeling approach
For a forecasting pipeline, consider:
Use horizon-matched IV as the main feature.
Add rolling realized volatility or a GARCH-family estimate.
Include bias correction or calibration for IV.
Validate across regimes, liquidity conditions, and forecast horizons.
Takeaway
IV should usually be your primary volatility signal, not your only one. A hybrid IV-plus-GARCH or IV-plus-realized-volatility model is generally more robust than choosing one input alone.
Are you using implied volatility features in your forecasting models? What has worked best in production?
Top comments (0)