Forecasting the return distribution using high-frequency volatility measures

- We model the quantiles of returns conditional on realized volatility measures.
- We forecast and evaluate the return distribution relative to the GARCH model.
- The forecasts outperform the GJR-GARCH for the S&P 500 futures returns.
- However, they do not outperform the GARCH (1, 1) in forecasting the bond returns.

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1, 1) model when the 30-year Treasury bond futures return is considered.