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

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  • Worapree Maneesoonthorn

    ()

  • Catherine S. Forbes

    ()

  • Gael M. Martin

    ()

Abstract

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 speci.es 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 returns and 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.

Suggested Citation

  • Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2013. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 28/13, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2013-28
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Brendan P.M. McCabe, 2018. "Approximate Bayesian forecasting," Monash Econometrics and Business Statistics Working Papers 2/18, Monash University, Department of Econometrics and Business Statistics.
    2. Bibinger, Markus & Neely, Christopher J. & Winkelmann, Lars, 2017. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Working Papers 2017-12, Federal Reserve Bank of St. Louis.

    More about this item

    Keywords

    and phrases: Dynamic price and volatility jumps; Stochastic volatility; Hawkes process; Nonlinear state space model; Bayesian Markov chain Monte Carlo; Global financial cri-;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises

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