Abstract
Jump–diffusion processes involving diffusion processes with discontinuous movements, called jumps, are widely used to model time-series data that commonly exhibit discontinuity in their sample paths. The existing jump–diffusion models have been recently extended to multivariate time-series data. The models are, however, still limited by a single parametric jump-size distribution that is common across different subjects. Such strong parametric assumptions for the shape and structure of a jump-size distribution may be too restrictive and unrealistic for multiple subjects with different characteristics. This paper thus proposes an efficient Bayesian nonparametric method to flexibly model a jump-size distribution while borrowing information across subjects in a clustering procedure using a nested Dirichlet process. For efficient posterior computation, a partially collapsed Gibbs sampler is devised to fit the proposed model. The proposed methodology is illustrated through a simulation study and an application to daily stock price data for companies in the S&P 100 index from June 2007 to June 2017.
Original language | English |
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Pages (from-to) | 439-453 |
Number of pages | 15 |
Journal | Journal of the Korean Statistical Society |
Volume | 48 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2019 Sept |
Bibliographical note
Publisher Copyright:© 2019 The Korean Statistical Society
All Science Journal Classification (ASJC) codes
- Statistics and Probability