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

Worapree Maneesoonthorn (), Catherine Forbes and Gael Martin ()

No 8/16, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: dynamic price and volatility jumps; stochastic volatility; Hawkes process; nonlinear state space model; Bayesian Markov chain Monte Carlo; global financial crises (search for similar items in EconPapers)
JEL-codes: C11 C58 G01 (search for similar items in EconPapers)
Pages: 35
Date: 2016
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:
Journal Article: Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures (2017)
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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