Using Category Functors to Forecast S&P 500 Volatility from Economic Events

Summary

This article models selected economic calendar releases as a graph, then treats the graph as a category whose vertices are objects and connections are morphisms. It proposes mapping this structure to a time ordered category of S&P 500 price-range volatility, calculated from each bar’s high and low rather than using VIX. Because several event pairings can map to the same volatility observation, the method combines separate mappings with weights to produce a forecast.

The article compares forecasts based on object mappings with forecasts based on morphism mappings, using the projections to adjust trailing stops. Its discussion builds on a prior NASDAQ example, but gives no conclusive performance evidence here; it notes that the earlier test covered only a short period and was too limited to rank approaches. The proposed economic relationships are illustrative rather than validated causal claims. The author suggests that a neural network could learn the mappings from more data, while noting the current approach uses simple incremental coefficients and leaves entry signals and position sizing as possible extensions.

Key ideas

• Economic calendar releases can be represented as vertices and edges in a graph, then treated as a category.
• The proposed codomain records S&P 500 bar range volatility rather than VIX.
• Multiple calendar objects may map to the same volatility observation, so their projections are combined with weights.
• The article compares mappings of objects with mappings of morphisms and uses forecasts to adjust trailing stops.
• The suggested economic links are illustrative, and the article provides no decisive evidence that the method forecasts volatility reliably.

Tags

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