Inference on Self-Exciting Jumps in Prices and Volatility Using High-Frequency Measures

• Activity Number: 686
• Type: Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Worapree Maneesoonthorn*
• Companies: The University of Melbourne
• Keywords: Bayesian ; Nonlinear state space model ; Stochastic volatility ; Hawkes process ; Global financial crisis ; Dynamic jumps

Abstract

Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components; with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. The calculation of marginal likelihoods for the proposed and related models is discussed. An extensive empirical investigation is undertaken using the S&P500 market index, with substantial support for dynamic jump intensities - including in terms of predictive accuracy - documented.