We had looked at using VIX for driving equity index positioning about eight years ago and had abandoned the idea having found no relationship between VIX levels and future returns (Part I, Part II). We found some modest success in using realized volatility to position towards a target volatility (Volatility Targeting). However, the idea of using VIX as a predictor of future equity returns refuses to die.
The correct time to take more equity risk is when VIX has been high for six months but has been trending down. The correct time to take less equity risk is when VIX has been low for six months but has been trending up. The target equity weight is then proportional to the target equity risk divided by VIX. Therefore, at most times, low VIX corresponds to high equity weight and high VIX to low equity weight.
Thanks to AI, we can now test this hypothesis out without wasting too much time. We got Claude to give us the long-only and long-short outlines and backtest plans (lo, ls). And then we got hermes + deepseek to execute this in R. The tl;dr is that inverse-volatility long-only is “good enough.”
The approach sounded good on the face of it but doesn’t bear scrutiny once you run the numbers through it.
Code and charts on github.