On Optimal Sample-Frequency and Model-Averaging Selection When Predicting Realized Volatility

Predicting volatility of financial assets based on realized volatility has grown popular in the literature due to its strong prediction power. Theoretically, realized volatility has the advantage of being free from measurement error since it accounts for intraday variation that occurs on high frequencies in financial assets. However, in practice, as sample-frequency increases, market microstructure noise might be absorbed and as a result lead to inaccurate predictions. Furthermore, predicting realized volatility based on single models cause predictions to suffer from model uncertainty, which might lead to understatements of the risk in the forecasting process and as a result cause poor predictions. Based on mentioned issues, this paper investigates which sample frequency that minimizes forecast error, 1-, 5- or 10-min, and which model-averaging process that should be used to deal with model uncertainty, Mean forecast combinations, Bayesian model-averaging or Dynamic model-averaging. The results suggest that a 1-min sample-frequency minimize forecast errors and that Bayesian model-averaging performs better than Dynamic model-averaging on 1-day and 1-week horizons, while Dynamic model-averaging performs slightly better on 2-weeks horizon