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

被引:24
作者
Maneesoonthorn, Worapree [1 ]
Forbes, Catherine S. [2 ]
Martin, Gael M. [2 ]
机构
[1] Univ Melbourne, Melbourne Business Sch, Melbourne, Vic, Australia
[2] Monash Univ, Dept Econometr & Business Stat, Melbourne, Vic, Australia
基金
澳大利亚研究理事会;
关键词
STOCHASTIC VOLATILITY; RISK PREMIA; REALIZED VOLATILITY; ECONOMETRIC-ANALYSIS; MARGINAL LIKELIHOOD; POINT-PROCESSES; OPTION MARKET; MODELS; RETURNS; COMPONENTS;
D O I
10.1002/jae.2547
中图分类号
F [经济];
学科分类号
02 ;
摘要
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 intensitiesincluding in terms of predictive accuracydocumented. Copyright (c) 2016 John Wiley & Sons, Ltd.
引用
收藏
页码:504 / 532
页数:29
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