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

Worapree Maneesoonthorn (), Catherine Forbes and Gael Martin

No 30/14, 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. The calculation of marginal likelihoods for the proposed and related models is discussed. An extensive empirical investigation is undertaken using the S&P500 market index, 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)
Date: 2014
New Economics Papers: this item is included in nep-mst and nep-ore
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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 (2016)
Working Paper: Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures (2013)
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