Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures
journal contributionposted on 2022-11-09, 02:22 authored by Worapree Maneesoonthorn, Catherine S. Forbes, Gael M. Martin
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. An evaluation of marginal likelihoods for the proposed model relative to a large number of alternative models, including some that have featured in the literature, is provided. An extensive empirical investigation is undertaken using data on the S&P500 market index over the 1996 to 2014 period, with substantial support for dynamic jump intensities -- including in terms of predictive accuracy -- documented.