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

2014/01/16 by Worapree Maneesoonthorn, Maneesoonthorn, Worapree, Catherine S. Forbes +4
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Insurance, Mortality, Demography, Risk Management #Statistical Finance (q-fin.ST) #q-fin.ST #stat.AP

paper · pdf · doi:10.48550/arxiv.1401.3911

openalex publication_date 2014/01/16 · arxiv created 2016/03/09 · arxiv updated 2016/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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. 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.

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