Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility

Abstract

This article estimates models of high-frequency index futures returns using “around-the-clock” 5-min 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 and is also useful for risk management and trading applications. Supplementary materials for this article are available online.

• Auxiliary particle filter
• Cubic smoothing spline
• Jump diffusion
• Markov chain Monte Carlo
• Realized volatility
• State-space model
• Stochastic volatility

SUPPLEMENTARY MATERIALS

The online supplementary materials contain prior sensitivity and MCMC convergence results: two-factor stochastic volatility models.