INFERENCE FOR STOCHASTIC VOLATILITY MODELS USING TIME CHANGE TRANSFORMATIONS

• 成果类型: Article
• 署名作者: Kalogeropoulos, Konstantinos; Roberts, Gareth O.; Dellaportas, Petros
• 署名单位: University of London; London School Economics & Political Science; University of Warwick; Athens University of Economics & Business
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/09-AOS702
• 发表日期: 2010
• 页码: 784-807
• 关键词: maximum-likelihood-estimation; diffusion-models; distributions; simulation; options

摘要

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrization defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates.