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Inference of Self-Exciting Jumps in Prices and Volatility Using High-Frequency Measures
Journal of Applied Econometrics (2016)
  • Worapree Ole Maneesoonthorn, Melbourne Business School
  • Catherine S Forbes, Monash University
  • Gael M Martin, Monash University
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. 

  • Dynamic price and volatility jumps; Stochastic volatility; Hawkes process; Nonlinear state space model; Bayesian Markov chain Monte Carlo; Global financial crisis. JEL Classifications: C11,
  • C58,
  • G01.
Publication Date
Winter 2016
Publisher Statement
Citation Information
Worapree Ole Maneesoonthorn, Catherine S Forbes and Gael M Martin. "Inference of Self-Exciting Jumps in Prices and Volatility Using High-Frequency Measures" Journal of Applied Econometrics (2016)
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