This paper investigates the dynamic behaviour of jumps in financial prices and volatility. The proposed model is based on a standard jump diffusion process for price and volatility augmented by a bivariate Hawkes process for the two jump components. The latter process specifies a joint dynamic structure for the price and volatility jump intensities, with the intensity of a volatility jump also directly affected by a jump in the price. The impact of certain aspects of the model on the higher-order conditional moments for returns is investigated. In particular, the differential effects of the jump intensities and the random process for latent volatility itself, are measured and documented. A state space representation of the model is constructed using both financial returnsand non-parametric measures of integrated volatility and price jumps as the observable quantities. Bayesian inference , based on a Markov chain Monte Carlo algorithm , is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities . An extensive empirical investigation using data based on the S&P500 market index over a period ending in early-2013 is conducted. Substantial empirical support for dynamic jump intensities is documented, with predictive accuracy enhanced by the inclusion of this type of specification. In addition, movements in the intensity parameter for volatility jumps are found to track key market events closely over this period .
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 .
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This paper investigates the dynamic behaviour of jumps in financial prices and volatility. The proposed model is based on a standard jump diffusion process for
Dynamic jumps in the
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augmented by a bivariate Hawkes process for the two jump components. The latter process specifies a joint dynamic structure for the price and volatility jump intensities, with the intensity of a volatility jump also directly affected by a jump in the price. The impact of certain aspects of the model on the higher-order conditional moments for returns is investigated. In particular, the differential effects of the jump intensities and the random process for latent volatility itself, are measured and documented.
of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion.
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of the model is constructed using both financial returnsand non-parametric
is used to link observed returns, plus nonparametric
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as the observable quantities. Bayesian inference , based on
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, is used to obtain a posterior distribution for the relevant model parameters and latent variables, and to analyze various hypotheses about the dynamics in, and the relationship between, the jump intensities
. The calculation of marginal likelihoods for the proposed and related models is discussed
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using data based on
is undertaken using
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over a period ending in early-2013 is conducted. Substantial empirical
, with substantial
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is documented, with predictive accuracy enhanced by the inclusion of this type of specification. In addition, movements in the intensity parameter for volatility jumps are found to track key market events closely over this period
- including in terms of predictive accuracy - documented