  This paper estimates models of high frequency index futures returns using
`around the clock' 5-minute returns that incorporate the following key
features: multiple persistent stochastic volatility factors, jumps in prices
and volatilities, seasonal components capturing time of the day patterns,
correlations between return and volatility shocks, and announcement effects. We
develop an integrated MCMC approach to estimate interday and intraday
parameters and states using high-frequency data without resorting to various
aggregation measures like realized volatility. We provide a case study using
financial crisis data from 2007 to 2009, and use particle filters to construct
likelihood functions for model comparison and out-of-sample forecasting from
2009 to 2012. We show that our approach improves realized volatility forecasts
by up to 50% over existing benchmarks.
