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

TL;DR

This paper develops a Bayesian high-frequency volatility model incorporating multiple features and uses MCMC and particle filters for estimation and forecasting, significantly improving volatility predictions during the financial crisis.

Contribution

It introduces an integrated Bayesian approach with MCMC and particle filters for high-frequency volatility modeling, capturing complex features without aggregation.

Findings

Forecasts improve realized volatility predictions by up to 50%.

Model effectively captures multiple volatility factors and jumps.

Outperforms existing benchmarks in out-of-sample forecasts.

Abstract

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.