Empirical Likelihood Methods Based on Characteristic Functions With Applications to Levy Processes

• 成果类型: Article
• 署名作者: Chan, Ngai Hang; Chen, Song Xi; Peng, Liang; Yu, Cindy L.
• 署名单位: Chinese University of Hong Kong; Iowa State University; Peking University; University System of Georgia; Georgia Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08349
• 发表日期: 2009
• 页码: 1621-1630
• 关键词: of-fit tests; WEAK-CONVERGENCE; parameters; approximation; symmetry; models

摘要

Levy processes have been receiving increasing attention in financial modeling. One distinctive feature of such models is that their characteristic functions are readily available. Inference based on characteristic functions is very useful for studying Levy processes. By incorporating the recent advances in nonparametric approaches, empirical likelihood methods based on characteristic functions are developed in this paper for parameter estimation, testing a particular parametric class including the presence of a jump component in the Levy process and testing for symmetry of a distribution. Simulation and case studies confirm the effectiveness of the proposed method.