Evaluating monthly volatility forecasts using proxies at different frequencies

•This paper analyzes the forecastability of the EuroStoxx 50 index monthly returns.•Punctual and aggregated monthly forecasts obtained from GARCH models using returns at different frequencies.•The models performance is measured with respect to alternative proxies for the unobserved volatility.•Monthly volatility proxies are obtained using mixed data sampling methods using daily frequency.•Robust results found across proxies support multi-step-ahead GARCH-type forecasts.

This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained from GARCH and AGARCH models with Normal and Student's t errors are evaluated with respect to proxies for the unobserved volatility obtained through sampling at different frequencies. It is found that aggregation of daily multi-step ahead GARCH-type forecasts provide rather accurate predictions of monthly volatility.