Calculating 20-Day Annualized Variance from Daily Stock Returns

Summary

The document explains a rolling estimate of annualized variance using daily stock returns. First compute each daily return from consecutive closing prices, then calculate the variance over a 20-trading-day window and scale it by 250, an assumed count of trading days per year. It also describes a BigQuant SQL implementation based on rolling standard deviation squared, which is mathematically equivalent to variance when the same convention is used.

The stated annualization factor is an assumption, so the resulting value depends on the chosen trading-day count and variance convention. The article recounts an implementation issue in which an attempted variance function was undefined and says the code was corrected after feedback. It offers a calculation recipe and platform example, not empirical evidence that the measure predicts returns or improves a strategy. The SQL’s date filter and data source are specific to the example, and the document does not discuss return adjustments or missing observations.

Key ideas

• Daily returns are computed from the change between consecutive closing prices.
• The method measures variance across a rolling window of 20 trading days.
• Annualization multiplies daily variance by an assumed 250 trading days per year.
• Squaring rolling standard deviation produces the variance used in the example.
• The article provides implementation guidance but no evidence of predictive or trading performance.

Tags

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