Module Specifications.
Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
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Repeat examination |
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Description This module aims to develop data analysis and modelling techniques for risk management for finance and insurance applications. Classical return and loss distributions will be described and techniques for choosing and fitting distributions to sample data will be presented. Models for aggregate claims data will be developed. Advanced topics in the use of copulas for modelling dependencies between risks and extreme value theory will be introduced. Exploratory data analysis and machine learning techniques for Big Data will be examined in the context of risk management. Practical analysis and modelling examples will be provided throughout the course using R. | |||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Identify and fit appropriate return and loss distributions using R. 2. Explain the use of reinsurance in risk management for insurance companies. 3. Develop risk models for aggregate claims. 4. Understand, fit, and forecast ARIMA time series models. 5. Use of copulas in modelling dependencies between risks. 6. Apply extreme value theory to insurance problems. 7. Understand CAPM and multifactor asset pricing models. Regressions and related tests. 8. Principal component analysis. Yield curve and futures modelling. 9. Apply simple machine learning techniques to big data for the insurance industry using R. | |||||||||||||||||||||||||||||||||||||||||||
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
Return and Loss DistributionsRisk management; classical return and loss distributions; proportional and excess of loss reinsurance; statistical tests; model fitting and checking.Aggregate Claims ModellingAggregate loss distributions; Compound Poisson distribution; allowance for reinsurance.Time Series ModelsARIMA models, fitting, and forecasting.CopulasMultivariate distributions; properties of copulas; concordance measures – Kendall’s tau and Spearman’s rho; Copula selection; Gaussian copula; Archimedean copula family; Elliptical copulas.Extreme Value TheoryExtreme value distributions; block maxima; exceedances; tail weights.Asset Pricing ModelsCAPM and multifactor models; tests of linear models; multivariate regressions and factors.Machine LearningSupervised and unsupervised machine learning algorithms; principal component analysis; regression and classification; applications of machine learning techniques in R. | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||