News arrival, jump dynamics, and volatility components for individual stock returns

• 成果类型: Article
• 署名作者: Maheu, JM; McCurdy, TH
• 署名单位: University of Toronto; University of Toronto
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/j.1540-6261.2004.00648.x
• 发表日期: 2004
• 页码: 755-793
• 关键词: exchange-rates; options; models; MARKET; crash; INFORMATION; variance; moments; IMPACT; volume

摘要

This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time-varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically into expected volatility. This model improves forecasts of volatility, particularly after large changes in stock returns. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, leverage effects, and the time-series dynamics of jump clustering.