Bayesian diffusion process models with time-varying parameters |
| |
Authors: | Yongku Kim Suk Bok Kang L. Mark Berliner |
| |
Affiliation: | 1. Department of Statistics, Yeungnam University, South Korea;2. Department of Statistics, The Ohio State University, United States |
| |
Abstract: | The diffusion process is a widely used statistical model for many natural dynamic phenomena but its inference is very complicated because complete data describing the diffusion sample path is not necessarily available. In addition, data is often collected with substantial uncertainty and it is not uncommon to have missing observations. Thus, the observed process will be discrete over a finite time period and the marginal likelihood given by this discrete data is not always available. In this paper, we consider a class of nonstationary diffusion process models with not only the measurement error but also discretely time-varying parameters which are modeled via a state space model. Hierarchical Bayesian inference for such a diffusion process model with time-varying parameters is applied to financial data. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|