A1359
Title: Bayesian modeling and forecasting of high-frequency volatility
Authors: Jonathan Stroud - Georgetown University (United States) [presenting]
Michael Johannes - Columbia University (United States)
Abstract: Models of high-frequency index futures returns are estimated by 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 and is also useful for risk management and trading applications.
