Module: Financial & Actuarial Models (Adv)

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Financial & Actuarial Models (Adv)
Module Code	MS549
School	School of Mathematical Sciences
Module Co-ordinator	Semester 1: Paolo Guasoni Semester 2: Paolo Guasoni Autumn: Paolo Guasoni
Module Teachers	Paolo Guasoni
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Repeat examination

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.

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.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	Classes
Laboratory	24	Practical Sessions
Independent Study	140	Independent work.
Total Workload: 188

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

Indicative Content and Learning Activities

Return and Loss Distributions
Risk management; classical return and loss distributions; proportional and excess of loss reinsurance; statistical tests; model fitting and checking.

Aggregate Claims Modelling
Aggregate loss distributions; Compound Poisson distribution; allowance for reinsurance.

Time Series Models
ARIMA models, fitting, and forecasting.

Copulas
Multivariate distributions; properties of copulas; concordance measures – Kendall’s tau and Spearman’s rho; Copula selection; Gaussian copula; Archimedean copula family; Elliptical copulas.

Extreme Value Theory
Extreme value distributions; block maxima; exceedances; tail weights.

Asset Pricing Models
CAPM and multifactor models; tests of linear models; multivariate regressions and factors.

Machine Learning
Supervised and unsupervised machine learning algorithms; principal component analysis; regression and classification; applications of machine learning techniques in R.

Assessment Breakdown
Continuous Assessment	25%	Examination Weight	75%

Type	Description	% of total	Assessment Date
Course Work Breakdown
In Class Test	Laboratory practical test	25%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Other Resources

None

Programme or List of Programmes

AMPD	PhD
AMPM	MSc
AMPT	PhD-track
MFM	MSc in Financial Mathematics

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