Inference on self-exciting jumps in prices and volatility using high-frequency measures

Worapree Maneesoonthorn, Catherine S. Forbes, Gael M. Martin

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

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&P 500 market index over the 1996-2014 period, with substantial support for dynamic jump intensities-including in terms of predictive accuracy-documented.

Original languageEnglish
Pages (from-to)504-532
Number of pages29
JournalJournal of Applied Econometrics
Volume32
Issue number3
DOIs
Publication statusPublished - Apr 2017

Keywords

  • Dynamic price and volatility jumps
  • Stochastic volatility
  • Hawkes process
  • Nonolinear state space model
  • Bayesian Markov chain Monte Carlo
  • Global financial crisis

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