Bachelor of Science Honours in Quantitative Risk Management

Purpose

The purpose of the Bachelor of Science Honours in Quantitative Risk Management is to prepare learners for research-based postgraduate study. It serves to deepen learners' expertise in the field of Quantitative Risk Management, and to develop research capacity in the methodology and techniques of the field of Quantitative Risk Management. The qualification demands a high level of theoretical engagement and intellectual independence.

Rationale

The qualification is intended to provide the country with qualifying learners who have specialised knowledge in Quantitative Risk Management and can, therefore, serve the country in a variety of important ways, in both the public and private sectors.

Qualifying learners will be prepared for specific occupational possibilities as qualified risk managers by obtaining knowledge and skills, as well as research abilities to examine and solve problems related to the discipline. This qualification prepares learners for research-based post-graduate studies at Level 9.

Recognition of Prior Learning (RPL)

Where learners do not meet the minimum admission requirements, RPL may be used to grant access to the qualifications. RPL will be applied according to the national Recognition of Prior Learning, Credit Accumulation and Transfer, Assessment (CHE 2016) and the institution RPL policies. The process will be managed by the Faculty.

The Faculty will ensure that quality assurance processes that address the specificities of the RPL process (including applications, assessment, and reporting and management systems) are implemented; and that administrative and support systems, both prior and after RPL assessment, are in place. Learners will be assessed against NQF Level 7 competencies.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Science in Quantitative Risk Management, NQF Level 7.

Or

• A Relevant Qualification in the related field, NQF Level 7.

This qualification consists of the following compulsory modules at National Qualifications Framework Level 8 totalling 148 Credits.

Compulsory Modules, 148 Credits

• Quantitative Risk Analysis I, 16 Credits.
• Quantitative Risk Analysis II, 16 Credits.
• Financial Engineering, 16 Credits.
• Investment Theory, 16 Credits.
• Statistical Data-analysis I, 12 Credits.
• Risk Management, 16 Credits.
• Statistical Data-analysis II, 12 Credits.
• Multivariate Statistics, 12 Credits.
• Research Module: Financial Engineering and Financial Modelling, 32 Credits.

1. Demonstrate depth of knowledge and skills to solve practical risk management problems. These skills are further developed and enhanced in the Master's Degree.
2. Formulate and solve problems using market and credit risk analysis, econometric and statistical time series, basic investment management principles, statistical analysis and financial engineering methodologies, models and techniques.
3. Formulate and solve problems in bank risk management.
4. Conduct and report research under supervision in the form of a discrete research component that is appropriate to the discipline or field of study.

The following Associated Assessment Criteria will be applied in an integrated manner across the Exit Level Outcomes

• Implement his/her specialist knowledge to analyse and evaluate market risk and explain the modelling and management of market risk in financial institutions.
• Develop/propose an integrated risk measurement (e.g. Value-at-Risk) framework by applying statistical methods and techniques.
• Explain the concepts of risk classification and analyse and criticise risk measurement concepts in financial risk management.
• Describe the relationship between systematic risk, non-systematic or specific risk, and concentration of risk.
• Describe the properties and limitations of common risk measures and recommend a specific choice of model based on the results of both quantitative and qualitative analysis of financial or insurance data.
• Formulate valuation problems in mathematical forms using appropriate notation.
• Critically evaluate modern financial theories and select the appropriate instruments for different risk management applications.
• Price simple derivative securities, using appropriate software, if applicable.
• Develop and communicate his or her ideas and opinions in well-formed arguments, using appropriate academic, professional, or occupational discourse.
• Track own learning progress and manage all resources successfully to realise all outcomes of the module.
• Discuss and develop portfolio investment strategies working individually or in groups.
• Think independently and solve complex portfolio choice problems, select assets and manage portfolios.
• Analyse and critically evaluate the performance of an investment manager.
• Develop solutions to corporate, risk and investment management problems.
• Develop and communicate his or her ideas and opinions in well-formed arguments, using appropriate academic, professional, or occupational discourse.
• Implement his/her specialist knowledge to analyse and evaluate financial instruments.
• Conduct and write a report regarding the current academic discourse on a specified financial instrument.
• Describe, formulate, apply, and critically examine a range of financial optimisation models, its assessment and selection, and the solution techniques associated with these models.
• Explain the relationship between a volatility smile and the risk-neutral probability measure used in binomial pricing.
• Explain the concepts of numerical methods used in Financial Engineering and Optimisation e.g. Least Squares Monte Carlo, Finite Differences for pricing exotic options found in insurance, Simplex Method for linear programming and Branch-and- Work independently and be well prepared for all seminars.
• Contribute to discussions during seminars and demonstrate knowledge of the relevant concepts and methods in various forms of assessment, by providing insight into - and solutions to - problems/questions with the correct use of terminology appropriate to the field of Financial Engineering and Optimisation.
• Analyse and implement financial engineering and optimisation models in software packages (e.g. SAS/IML and MS Excel).

Integrated Assessment

Formative assessment will be undertaken through projects, assignments, case studies and tests during the year in the modules. In some modules, formative assessment will include portfolios, practical evaluations and a research proposal.

Learners will also write a summative and integrated exam (written and practical) and will submit a research report on discrete research that was conducted under supervision at the end of the year for summative assessment.