Journal article

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

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&P 500 market index over the 1996–2014 period, with substantial..

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University of Melbourne Researchers


Awarded by Australian Research Council

Funding Acknowledgements

The authors would like to thank three anonymous referees and a co-editor for very detailed and constructive comments on earlier drafts of the paper. We also thank Yacine Ait-Sahalia, John Maheu, Eric Renault, George Tauchen, Victor Todorov and Herman van Dijk for very constructive comments at various stages in the development of the paper, plus the participants at the Society of Financial Econometrics Annual Conference, 2014, the International Association for Applied Econometrics Conference, 2014, and the Econometric Society Australasian Meetings, 2014. The research has been supported by Australian Research Council Discovery Grant DP150101728.