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This paper estimates models of high frequency index futures returns using`around the clock' 5-minute returns that incorporate the following keyfeatures: multiple persistent stochastic volatility factors, jumps in pricesand volatilities, seasonal components capturing time of the day patterns,correlations between return and volatility shocks, and announcement effects. Wedevelop an integrated MCMC approach to estimate interday and intradayparameters and states using high-frequency data without resorting to variousaggregation measures like realized volatility. We provide a case study usingfinancial crisis data from 2007 to 2009, and use particle filters to constructlikelihood functions for model comparison and out-of-sample forecasting from2009 to 2012. We show that our approach improves realized volatility forecastsby up to 50% over existing benchmarks.

Bayesian modeling and forecasting of 24-hour high-frequency volatility: A case study of the financial crisis