A Cleaner Way to Compute Seasonal Vol

Key Takeaways

• Calendar-month RV avoids overlapping earnings spikes
• Method uses daily log returns, annualizes via √252
• Produces single volatility figure per month per year
• Reduces bias from rolling windows in seasonality studies
• Results align with prior findings, confirming robustness

Pulse Analysis

Seasonality research often relies on rolling realized volatility, but that technique can inadvertently double‑count extreme events, especially earnings announcements that dominate a single month. The calendar‑month realized volatility (CMRV) method sidesteps this flaw by aggregating daily log returns within each calendar month, then scaling by the square root of 252 trading days. This produces an unambiguous, month‑specific volatility metric that reflects true market dynamics without the smearing effect of overlapping windows.

For practitioners, the CMRV approach offers a cleaner input for quantitative models that forecast risk, price options, or allocate capital based on seasonal patterns. By isolating each month’s volatility, analysts can more precisely attribute spikes to macro‑economic releases, sector‑specific news, or firm‑level earnings, enhancing the granularity of risk attribution. The method also simplifies data pipelines: a single pass over daily returns yields a tidy time series of monthly volatilities, reducing computational overhead and potential sources of error.

Empirical checks in the updated notebook confirm that the seasonal volatility narrative remains intact. The same months that historically exhibit heightened risk—such as August earnings season—still stand out, but now the magnitude is not inflated by repeated counting. This validation reassures investors that prior strategies built on rolling volatility remain robust, while offering a more transparent framework for future research and portfolio construction.