High-frequency financial data are not only discretely sampled in time but the time
separating successive observations is often random. We review the paper of Aït-Sahalia
and Mykland (2003), that measure the effects of discreteness sampling and ignoring the
randomness of the sampling for estimating the m.l.e of a continuous-time diffusion model.
In that article, three different assumptions and restrict in one made on the sampling intervals,
and the corresponding likelihood function, asymptotic normality, and covariance
matrix are obtained. It is concluded that the effects due to discretely sampling are smaller
than the effect of simply ignoring the sampling randomness. This study focuses on rechecking
the results in the paper of A¡Lıt-Sahalia and Mykland (2003) including theory, simulation
and application. We derive a different likelihood function expression from A¡Lıt-Sahalia and
Mykland (2003)¡¦s result. However, the asymptotic covariance are consistent for both approaching
in the O-U process. Furthermore, we conduct an empirical study on the high
frequency transaction time data by using non-homogeneous Poisson Processes.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0804110-144650 |
| Date | 04 August 2010 |
| Creators | Tsai, Yi-Po |
| Contributors | Shih-Feng Huang, Mong-Na Lo Huang, Mei-Hui Guo, May-Ru Chen, Fu-Chuen Chang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0804110-144650 |
| Rights | unrestricted, Copyright information available at source archive |
Page generated in 0.0023 seconds