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 modelsis 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.
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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The calculation
An evaluation
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and related modelsis discussed
model relative to a large number of alternative models, including some that have featured in the literature, is provided
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data on
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over the 1996 to 2014 period