Registry
Module Specifications
Archived Version 2013 - 2014
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Description The module introduces the main concepts underlying the analysis of Time Series models, studying the stationarity of linear time series and some related models. It also includes an introduction to Monte Carlo simulation. It cover the syllabus of the Time Series part of the Institute and Faculty of Actuaries Core Technical subject CT6, giving students of actuarial programmes an opportunity to be recommened for an exemption from the professional examination in this Core Technical subject. It is an advanced level undergraduate course with a substantial theoretical component, and provides the platform for further advanced courses in financial econometrics. | |||||||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. prove whether given time series models are weakly or strictly stationary 2. establish the important properties of moving average models, and to apply them to model financial phenomena 3. characterise the class of linear autoregressive models which possess unique attracting stationary solutions, and to apply these processes to model financial phenomena 4. reduce time series data and models to the stationary case, and to decide whether certain data sets fit a given stationary linear time series model 5. model multidimensional discrete time stochastic economic phenomena, and analyse these models as vector autoregressive models 6. establish the validity of important general methods for generating random variates, to apply these methods, and analyse their efficiency | |||||||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities Stationary processesStrict and weak stationary, autocovariance function, integrated time series. Linear time series models. Wold's decomposition theorem. Partial autocorrelation function. CT6 xi 1-11Moving average time seriesStationarity and invertibility of moving average models. Invertibility of general linear processes. Applications to modelling inefficient financial markets CT6 xi 1-11Linear autoregressive time seriesAR(p) time series. Characterisation of stationarity. Stationary solutions and uniqueness. Applications to volatility and interest rate modelling. ARMA(p,q) models, in particular ARMA(1,1). ARIMA models.Data analysis of Time SeriesReducing time series to stationary series. Box-Jenkins method for fitting linear time series. Statistical testing for white noise, moving average, autoregressive models. Forecasting. CT6 - [ix] 12 -13Multidimensional and further time series modelsMultidimensional covariance function. Multidimensional white noise. Vector autogressive (VAR) processes. Stationarity and cointegration. Using VAR to model dynamic economic phenomena. Properties and applications of ARCH type models.Monte Carlo simulationPseudo-random number generation. Generation of random variates. Variance reduction. Simulation of time series. Reliability of simulation. CT6 - [x] | |||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||||||
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