2015/16 Taught Postgraduate Module Catalogue

LUBS5007M Statistical Models

15 creditsClass Size: 60

Module manager: Dr Iain Clacher
Email: ic@lubs.leeds.ac.uk

Taught: 1 Jun to 31 Jul View Timetable

Year running 2015/16

This module is not approved as an Elective

Objectives

On completion of the module students should be able to:

- demonstrate a comprehensive knowledge and understanding of key issues and techniques that are relevant to finance and risk management to an advanced level
- understand and appreciate contemporary theoretical and empirical literature in finance and risk management
- critically appreciate the theories and techniques applied in with a view to undertaking advanced academic and professional research in finance, risk management, insurance and pensions.

Learning outcomes
Students will be able to develop an understanding of models of survival and uncertainty and how these factors impact on the future cash flows of various pension and annuity products.

They should also be able to apply these techniques to practical problems and describe answers both mathematically as well as descriptively.

Skills outcomes
On completion of the module students are expected to be able to communicate both verbally and in writing the theoretical and applied the mathematical techniques required to estimate insurance statistics.

Syllabus

- Decision theory and its applications
- Probabilities and moments of loss distributions both with and without limits and risk-sharing arrangements
- Risk models involving frequency and severity distributions
- Estimation of the moment generating function and the moments for the risk models both with and without simple reinsurance arrangements
- Ruin for a risk models. Including estimation of the adjustment coefficient and Lundberg’s inequality
- Probability of ruin, changing parameter values simple reinsurance arrangements
- Bayesian statistics and calculating Bayesian estimators
- Describing and applying techniques for analysing a delay (or run-off) triangle and projecting the ultimate position
- Generalised linear model (GLM) and the analysis of time series models
- Concepts of “Monte Carlo” simulation using a series of pseudo-random numbers.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	2.00	20.00
Seminar	10	1.00	10.00
Private study hours			120.00
Total Contact hours			30.00
Total hours (100hr per 10 credits)			150.00

Private study

- Pre-lecture reading 2 h each (40 h)
- Post-lecture reading 2 h each (40 h)
- Seminar reading and preparation (40 h).

Opportunities for Formative Feedback

Student progress will be monitored through participation and performance in seminars.

Methods of assessment

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	3 hr	100.00
Total percentage (Assessment Exams)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 23/05/2012

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