On the measurement and forecasting of sales volatility: is the quantile approach better?

This paper asks how best to estimate and forecast firms’ residualized sales growth volatility, a standard measure of idiosyncratic uncertainty. Using a comprehensive dataset of Portuguese firms from 2006 to 2022, I compare the most common approaches used in the literature with a novel quantile-based method that exploits past cross-sectional information and contemporaneous macroeconomic variables and adjusts for the predictability in sales growth rates. I then estimate forecasting models and conduct a simulation exercise to assess the in-sample and out-of-sample performance of all approaches. The paper contributes to the literature by showing that quantile-based estimates and forecasts outperform traditional methods and that sales growth volatility can be measured with reasonable precision, making it suitable for wider application in empirical work. These findings support the application of quantile-based volatility measures to other low-frequency economic variables, especially those characterized by fat-tailed distributions.