Volatility Forecasting for Position Sizing and the VIX

Summary

The document examines whether improving volatility forecasts is worth the effort when volatility estimates are used to scale trading positions inversely. It contrasts basic estimates based on recent realized volatility with more involved approaches, including implied volatility, high-frequency data, GARCH or stochastic volatility models, and a blended estimate using a fast exponentially weighted average with a slower average. The forecast horizon is framed around how long positions are expected to be held.

To estimate the value of better forecasting, the author proposes comparing a standard backtest with one using future realized volatility over the next month, treating the latter as a perfect-foresight benchmark. The described simulation uses a portfolio of liquid futures and combines carry with several trend signals. The author reports the short answer that better volatility prediction is worthwhile, and plans to assess window choices, exponential averages, and the VIX as a possible additional indicator. However, the excerpt gives no performance figures or detailed comparison results, so it does not establish how large the benefit is or whether the VIX adds value.

Key ideas

• Inverse volatility scaling adjusts position sizes based on a forecast of volatility over the expected holding period.
• Forecasting approaches range from recent realized volatility to implied volatility and statistical volatility models.
• A perfect-foresight backtest using future realized volatility can serve as an upper-bound comparison.
• The proposed test combines carry and trend signals across liquid futures.
• The excerpt states that better forecasting helps but provides no measured results for the VIX or alternative methods.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.