BAYES FACTORS FOR DISCRETE OBSERVATIONS FROM DIFFUSION-PROCESSES

• 成果类型: Article
• 署名作者: POLSON, NG; ROBERTS, GO
• 署名单位: University of Cambridge
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• 发表日期: 1994
• 页码: 1126
• 关键词: stock-prices

摘要

We present an approach to model selection for a time series of data on a fine time scale. The underlying process generating the data is modelled as a continuous time stochastic process. The underlying continuous processes are assumed to be diffusions with time varying drift and diffusion coefficient. Several approaches to modelling the diffusion coefficient are described. To perform model selection, we propose an approximation to the Bayes factor that uses only the discrete data. We illustrate our approach for several well-known processes including: Brownian motion with drift, the Ornstein-Uhlenbeck process, a mean reversion process with drift, exponential Brownian motion, and a logistic growth model. Finally, we apply our technique to data from the Standard and Poor's 500 stock index by comparing a random walk to a mean reversion model.