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

Listed author(s):
  • 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. 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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File URL: http://business.monash.edu/__data/assets/pdf_file/0006/327183/wp30-14.pdf
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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 30/14.

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Date of creation: 2014
Handle: RePEc:msh:ebswps:2014-30
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