A Bayesian analysis of return dynamics with Levy jumps

• 成果类型: Article
• 署名作者: Li, Haitao; Wells, Martin T.; Yu, Cindy L.
• 署名单位: University of Michigan System; University of Michigan; Cornell University; Iowa State University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhl036
• 发表日期: 2008
• 页码: 2345
• 关键词: stochastic volatility; likelihood inference; options; diffusion; implicit; RISK

摘要

We have developed Bayesian Markov chain Monte Carlo (MCMC) methods for inferences of continuous-time models with stochastic volatility and infinite-activity Levy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Levy jumps, and (ii) the affine jump-diffusion (AJD) models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the AJD models fail to capture the infinitely many small Levy jumps, which are too big for Brownian motion to model and too small for compound Poisson process to capture. Empirical studies show that infinite-activity Levy jumps are essential for modeling the S&P 500 index returns.