Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)

• 成果类型: Article
• 署名作者: Beskos, Alexandros; Papaspiliopoulos, Omiros; Roberts, Gareth O.; Fearnhead, Paul
• 署名单位: Lancaster University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2006.00552.x
• 发表日期: 2006
• 页码: 333-361
• 关键词: STOCHASTIC DIFFERENTIAL-EQUATIONS; monte-carlo evaluation; maximum-likelihood; time; em; models; inference; volatility; simulation; variance

摘要

The objective of the paper is to present a novel methodology for likelihood-based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.