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STAT40680

Academic Year 2024/2025

Stochastic Models (STAT40680)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Fengnan Gao
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module covers part of the syllabus for the professional examination, Models (CS2), of the Faculty and Institute of Actuaries (UK) and is twinned with the module, Models - Survival Models. The module treats the principles behind, and key characteristics, of stochastic models in actuarial applications. Topics treated include: introduction to stochastic modelling; conditional expectation and conditional distribution, martingales, random walks, Markov chains with actuarial applications, continuous time Markov processes, Brownian motion with applications to mathematical finance, Poisson processes.

About this Module

Learning Outcomes:

The student will be able to define and classify stochastic models, judge when a model is satisfactory and be able to outline how to construct a stochastic model. The student will be able to apply Markov chains and Markov jump processes to actuarial applications, solving for their short run and long run behaviour. The student will be able to calibrate models from data, test the appropriateness of models, judge their overall suitability and simulate their behaviour.

Indicative Module Content:

Student Effort Hours:
Student Effort Type Hours
Lectures

18
Tutorial

10
Autonomous Student Learning

89
Total

117

Approaches to Teaching and Learning:
Lectures, tutorials, enquiry and problem-based learning.

Requirements, Exclusions and Recommendations
Learning Requirements:

The student will have an aptitude for mathematics, with a profligacy to 1st years honours level, and have taken level 2 modules in statistics.

Learning Recommendations:

Knowledge of probability theory and basic statistics to the level of the Probability Theory (STAT20110) and Inferential Statistics (STAT20100) course. Good knowledge in calculus (integrals, differentials) and linear algebra (vectors, matrices, eigenvalues).


Module Requisites and Incompatibles
Incompatibles:
STAT40210 - Stochastic Models


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): 2 hour end of semester exam End of trimester
Duration:
2 hr(s)
 Graded No
80
 No
Exam (In-person): 1 hour midterm (wk 7 usually) End of trimester
Duration:
1 hr(s)
 Graded No
20
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Ms Niamh Fennelly Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Fri 14:00 - 15:50
Autumn Tutorial Offering 1 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Thurs 15:00 - 15:50
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