A Bayesian Analysis of Return Dynamics with Stochastic Volatility and Levy Jumps

摘要：

We develop Bayesian Markov chain Monte Carlo methods for inferences of continuous-time models with stochastic volatility and infinite-activity Lévy jumps using discretely sampled data. Simulation studies show that (i) our methods provide accurate joint identification of diffusion, stochastic volatility, and Lévy jumps, and (ii) affine jump-diffusion models fail to adequately approximate the behavior of infinite-activity jumps. In particular, the affine jump-diffusion models fail to capture the infinitely many small Lévy 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 Lévy jumps are essential for modeling the S&P 500 index returns.

作者: Haitao Li Matin T. Wells Cindy L. Yu

作者单位: Stephen M. Ross School of Business, University ofMichigan, Ann Arbor, MI 48109 Department of Biological Statistics and Computational Biology and the Department of Social Statistic Department of Statistics, Iowa State University, Ames, IA 50011

会议类型: 国际会议

会议名称: 2005年中国金融国际年会

会议地点: 昆明

会议语种:英文

页码: 1-51

在线出版日期: 2005-07-05（万方平台首次上网日期，不代表论文的发表时间）