Financial Time Series Models and Applications

dc.contributor.authorHu, Mingming
dc.contributor.examiningcommitteeGhahramani, Melody (Statistics) Pai, Jeffrey (Warren Centre for Actuarial Studies)en
dc.contributor.supervisorLeblanc, Alexandre (Statistics) Thavaneswaran, A. (Statistics)en
dc.date.accessioned2011-01-19T16:56:14Z
dc.date.available2011-01-19T16:56:14Z
dc.date.issued2011-01-19T16:56:14Z
dc.degree.disciplineStatisticsen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractDuration models are often concerned with time intervals between trades, longer durations indicating a lack of trading activities. In this thesis, we study parameter estimation for the Autoregressive Conditional Duration (ACD) and Stochastic Conditional Duration (SCD) models. Maximum likelihood methods can usually be used in the case of ACD models. However, the SCD models are based on the assumption that durations are generated by a dynamic stochastic latent variable which is often perturbed by Exponential, Weibull, Gamma or Log-Normal distributed innovations. This makes the use of maximum likelihood methods difficult. One alternative method of parameter estimation, in this case, consists in using quasi-maximum likelihood after transforming the original nonlinear model into a state-space model and using the Kalman filter, a similar filtering scheme or the Generalized Method of Moments (GMM). We use the nonlinear filter and GMM method to analyze the Quadratic Stochastic Conditional duration model as well.en
dc.description.noteFebruary 2011en
dc.format.extent1667478 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1993/4373
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectACD Modelsen
dc.subjectStochastic Duration Modelen
dc.subjectQuadratic SCD Modelen
dc.subjectKalman Filteren
dc.subjectGMMen
dc.titleFinancial Time Series Models and Applicationsen
dc.typemaster thesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
secondintroduction1.pdf
Size:
1.59 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.33 KB
Format:
Item-specific license agreed to upon submission
Description: