A Bayesian Analysis of Return Dynamics with Lévy Jumps
开发了贝叶斯MCMC方法，用于识别含无限活跃度Lévy跳跃的连续时间模型，发现仿射跳跃扩散模型无法充分刻画小跳跃行为，实证表明Lévy跳跃对建模标普500指数收益至关重要。
We have developed Bayesian Markov chain Monte Carlo (MCMC) 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) 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 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. The Author 2006. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.