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

2014/01/16 by Maneesoonthorn, Worapree, Forbes, Catherine S., Martin, Gael M.
#Applications (stat.AP) #FOS: Computer and information sciences #FOS: Economics and business #Statistical Finance (q-fin.ST)

paper · doi:10.48550/arxiv.1401.3911

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.

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