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Title: Financial Time Series Models and Applications
Authors: Hu, Mingming
Supervisor: Leblanc, Alexandre (Statistics) Thavaneswaran, A. (Statistics)
Examining Committee: Ghahramani, Melody (Statistics) Pai, Jeffrey (Warren Centre for Actuarial Studies)
Graduation Date: February 2011
Keywords: ACD Models
Stochastic Duration Model
Quadratic SCD Model
Kalman Filter
Issue Date: 19-Jan-2011
Abstract: Duration 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.
Appears in Collection(s):FGS - Electronic Theses & Dissertations (Public)

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