Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures
用联合霍克斯过程与双变量跳跃扩散模型刻画资产价格和波动率的动态跳跃，结合高频数据与贝叶斯方法进行推断，并基于标普500指数数据验证了动态跳跃强度的预测优势。
Summary 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 intensities—including in terms of predictive accuracy—documented. Copyright © 2016 John Wiley & Sons, Ltd.