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. Thus, 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. Thus, risk 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. Analysing market conditions, industry needs and the emerging regulatory requirements, shows that there is a fast growing requirement global requirement for professionals with skills in risk and optimisation modelling. In particular, in the finance industry, banks, insurance companies and pension fund managers amongst others, have to comply with the requirements to quantify, control and report 'enterprise risk'. Recently, similar requirements have been introduced in the London Stock Exchange for the non-financial corporate sector whereby all listed companies have an obligation to report their risk exposure. 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. The MSc will pursue theoretical and applied research issues encompassing the growing use of risk and optimisation in diverse sectors.
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. 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. The 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.
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
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.
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 mid-September.
The Department currently has over 50 PhD students of whom about 30 are working in areas related to the course (optimisation, financial planning and risk, market risk , credit risk, optimum risk decisions and simulation). Our research focuses on optimum risk decisions, risk measures and the quantification of risk and return. The research is variously funded by the public sector as well as private sector financial institutions. Substantial support is provided by national and international organisations such as European Union, the research councils, EPSRC, Department of Trade and Industry, Unilever, UBS Investment, Fidelity Investments and MB Risk Management. The value of current research grants is in excess of £1.5 million
The postgraduate prospectus contains details of library, welfare, sports and other facilities. The University Computer Centre, based on a network of Sun workstations and Desk Top as well as Notebook PCs, provides access to specialist computers, national and international databases and electronic mail networks. CARISMA provides computing facilities and software tools and modelling systems. and a considerable research base for students of the course to call upon. Thus an extensive collection of software systems for modelling, optimisation and risk analysis is available to the students and the research community.
Assessment is by a combination of coursework and examination. Examinations are held at the end of May. The dissertation is of a practical nature and related to the focus of the course. Each student working on their dissertation will have a university supervisor and, in many cases, an industrial supervisor as well.
Students admitted to the Postgraduate Diploma course, who perform well in the assessments, may be permitted to transfer to the master's course and proceed to a dissertation. Conversely, students on the Master's course, whose performance is below the standard required for a master's degree, may be considered for the award of Postgraduate Diploma.