Forecasting volatility: Roles of sampling frequency and forecasting horizon

This study empirically tests how and to what extent the choice of the sampling frequency, the realized volatility (RV) measure, the forecasting horizon and the time-series model affect the quality of volatility forecasting. Using highly synchronous executable quotes retrieved from an electronic trading platform, the study avoids the influence of various market microstructure factors in measuring RV with high-frequency intraday data and in inferring implied volatility (IV) from option prices. The study shows that excluding non-trading-time volatility produces significant downward bias of RV by as much as 36%. Quality of prediction is significantly affected by the forecasting horizon and RV model, but is largely immune from the choice of sampling frequency. Consistent with prior research, IV outperforms time-series forecasts; however, the information content of historical volatility critically depends on the choice of RV measure.