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Financial Time Series
In many applications, in particular in finance and economics, observed data series often exhibit a behaviour which cannot be modelled with linear time series models (i.e. ARMA processes). Thus alternative models allowing for a nonlinear behaviour are called for and are successfully used. For instance, Robert Engle was awarded the Nobel Prize in 2003 for introducing the so-called (G)ARCH model. In this course we will first review some materials on linear time series methods, then consider and analyse several classes of nonlinear time series models, such as GARCH, Markov-switching as well as threshold autoregressive time series models. We study their common probabilistic and statistical concepts and theory (Markov chains with uncountable state space, stochastic recurrence equations, ergodicity and mixing). Finally, we will derive and apply estimators for the model parameters.STAT445 course is required for the BSc (Hons) in Financial Engineering.
The Courses will: introduce data collection and sampling techniques introduce simple random sampling, stratified sampling and cluster sampling designs along with their strengths and weaknessesintroduce the use of the statistics software package R You will be able to:describe and conduct appropriate statistical sampling techniquesbe able to interpret the model results in such a way that a non-user of statistics can understanduse R competentlywrite a scientific and technical repo
Subject to approval of the Head of School.
School of Mathematics and Statistics Postgraduate Handbook
General information for students
Domestic fee $1,017.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics