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

Worapree Maneesoonthorn, Catherine Forbes and Gael Martin

Papers from arXiv.org

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&P500 market index over the 1996 to 2014 period, with substantial support for dynamic jump intensities - including in terms of predictive accuracy - documented.

Date: 2014-01, Revised 2016-03
New Economics Papers: this item is included in nep-ets and nep-mst
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures (2016)
Working Paper: Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures (2014)
Working Paper: Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1401.3911

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