MSc Modelling and Management of Risk

Optimum planning and scheduling have been long standing goals in many private sector companies and public organisations. Organisations, however, face uncertainty, typically in demands, resource availability and yields. Within the stochastic environment, the goals of maximum profit (return) in the private sector or best service level in the public sector are not always achievable as predicted by deterministic models. In practice this translates directly into risk.

 

Risk, therefore, may appear in many forms involving profit, liquidity, market share, service level and can be attributed to changes in economic conditions, the environment, accidents and disasters.

 

It is very likely that similar risk control regulations will be introduced in the public sector and utility sectors covering health, transport, energy and telecommunications. It is therefore necessary to extend the concept of optimisation and introduce mathematical models with quantifiable risk that can be used for optimisation.

 

Aims

Analysing market conditions, industry needs and the emerging regulatory requirements, shows that there is a fast growing global requirement for professionals with skills in risk and optimisation modelling.

 

This MSc will pursue theoretical and applied research issues encompassing the growing use of risk and optimisation in diverse sectors.

 

Students on this course will gain specific skills in the areas of:

 

problem structuring

data analysis

building decision models

risk assessment in the corporate, financial, public and environment sectors

decision making under uncertainty

business simulation

project evaluation

resource management

recognising the areas where business analysis can add value

selecting appropriate types of analyses and applying them in an appropriate context.

 

 

Course Content

Modules (all core)

 

Risk and Risk Regulations

This module introduces the nature of financial risk, corporate risk, environmental risk, hazard risk and how these risks are quantified and used to make risk decisions. The regulatory framework for reporting risk is introduced and risk management for organisations and public policy issues including environmental risk are covered. The module also studies risk perception, risk aversion and risk assessment in terms of corporate and environmental impact.

 

Applied Risk and Optimisation in Financial Planning

This module introduces advanced modelling techniques in linear and integer programming (LP and IP) and illustrates how an industrial software package can be used for investigating the LP/IP problems. Similarly, properties of cash flow streams are analysed and investigated using spreadsheet software. The concepts of optimum allocation of financial resources under uncertainty are studied and the basic issues of financial planning and the models that provide a mathematical description of these investment problems are introduced. Finally, financial risk measures and how they can be incorporated in financial planning models are detailed.

 

Financial Risk Management

Main topics of study include: corporate financial performance, multi discriminate analysis, corporate failure and health, corporate governance, control risk self assessment, control theory, audit, treasury management, interest rate risk, foreign exchange risk, country risk assessment, investment risk, insurance, business opportunity and risk, investment appraisal using sensitivity analysis and scenario planning, forensic accounting, contract control and outsourcing.

 

Risk, Simulation and Decision Analysis

This module illustrates simulation as a decision making tool. All the relevant statistical concepts for simulation are introduced as well as basic decision theory. Case studies are used to show how risk can be modelled in a variety of areas and how re-engineering of the decision models can lead to less risk exposure. In particular the module covers case studies in project management, marketing, finance and waste management. Part of the course looks at the issue of gaining confidence in decision models using validation and verification.

 

Dissertation

Preparation for the dissertation starts early in the spring term so that a productive start can be made at the end of May. Students have a university supervisor with whom regular discussions are held. In addition, for the dissertations carried out in collaboration with an outside organisation, students will have a company supervisor. Dissertations are submitted by the end of September.