An Honours degree is frequently deemed to be the minimum professional entry requirement in the field, and demand for this qualification is high.
The number of students selected will be influenced by, among other things, staff capacity and the availability of resources within the Department, as well as academic merit and University transformation objectives. As staff capacity and resources may fluctuate from year to year, the number of students selected can also differ from year to year.
If the Financial Risk Management or Mathematical Statistics background of the applicant is deemed insufficient after a case-by-case determination by the Department of Statistics and Actuarial Science, the Department may require an additional departmental assessment on third year level Financial Risk Management and Mathematical Statistics topics. Students may also be required to complete additional undergraduate Stellenbosch University Financial Risk Management and Mathematical Statistics modules along with their Honours studies.
See the Faculty Calendar (Yearbook) here for detail of the more detailed Honours admission requirements.
Students will be required to pass modules totalling at least 120 credits made up as follows:
| Module | Code | Semester | Credits |
|---|---|---|---|
| Financial Risk Management | 54690-778 | 1 & 2 | 120 |
| Module | Code | Semester | Credits |
|---|---|---|---|
| Financial Risk Management A | 10459-731 | 1 | 12 |
| Financial Risk Management B | 10460-761 | 2 | 12 |
| Portfolio Management Theory A | 10660-733 | 1 | 12 |
| Portfolio Management Theory A | 10661-763 | 2 | 12 |
| Practical Financial Modelling | 11166-734 | 1 | 6 |
| Research Assignment: Financial Risk Management | 11218-793 | 1 & 2 | 30 |
| Stochastic Simulation | 65250-718 | 1 | 12 |
| Time Series Analysis B | 10751-747 | 2 | 12 |
Objectives and content (FRM-A)
The following topics are covered: Different models to estimate volatility, covariances and correlations of financial time series. Value-at-Risk (vaR): Definitions, foundations of VaR measurement, Decomposition of VaR, Parametric Linear VaR Models (normal- and t- distributions). Martingales and Measures: Market price of risk, Equivalent martingale measure result, Change of numeriare and applications. Interest Rate Derivatives, the Standard Market Model: Bond Options, Interest Rate Caps and Floors, European Swap options. Convexity-, timing- and quanto adjustments.
Objectives and content (FRM-B)
The following topics are covered: Interest Rate Derivatives – Models of the Short Rate: Equilibrium models, No Arbitrage models, Options on bonds, Volatility structures, Interest rate tree-building models. The Heath, Jarrow and Morton model. The Libor Market Model. Rachet-, Sticky- and Flexi caps and European Swap options. Credit risk – Different models to estimate default probabilities: Historical default probabilities, Using Bond prices, Using equity prices, Gaussian copula models, Binomial models, Merton’s model, KMV Approach. Credit VaR. Credit default swaps.
Objectives and content (PMT-A)
This module has been compiled in such a manner that it provides to the student an overview of the nature and scope of Portfolio Management as a subject and its application in practice. Its contents is loosely based on that of the first halve of the CFA level III exam, and the major topics include: the portfolio management process, the investment policy statement, portfolio management for individual and institutional investors, capital market expectations, asset allocation, financial statement analysis, equity analysis, and equity portfolio management.
Objectives and content (PMT-B)
This module has been compiled in such a manner that it provides to the student an overview of the nature and scope of Portfolio Management as a subject and its application in practice. Its contents is loosely based on that of the second halve of the CFA level III exam, and the major topics include: fixed income portfolio management, alternative investments portfolio management, portfolio risk management, execution of portfolio decisions, monitoring and rebalancing, evaluating portfolio performance, and behavioural finance.
Objectives and Content
In this module an introduction is given to Extreme Value Theory (EVT) and its role in Financial Risk Management. EVT entails the study of extreme events and for this theory has been developed to describe the behaviour in the tails of distributions. The module will disduss the theory in a conceptual fashion without proving the results. It will be shown how this theory can be used to carry out inferences on the relevant parameters of the underlying distribution. Both the classical approach of block maxima based on the Fisher-Tippett Theorem and the more modern threshold approach based on the Pickands-Balkema-de Haan Theorem will be discussed and applied. Results for both independent and dependent data will be covered.
Objectives and content
The main aim of this module is to teach students how to apply Excel and VBA to solve financial risk problems as well as to teach them some R programming. The following topics are covered with respect to Excel and VBS: An introduction to Excel an VBA; Excel basics and necessities; Using Excel to value bonds and swaps and to determine yield curves; Dates, tables and some Statistical applications in Excel; Applying Excel’s Solver; Some VBA programming; Portfolio optimisation with Excel and VBA; Black Scholes pricing of European options and calculating Greeks with Excel and VBA; Delta hedging with Excel and VBA. The R component of the module is an introduction to programming and data analysis within the R open source environment.
Objectives and content
This module is a continuation of undergraduate time series analyses and concentrates on more advanced forecasting techniques. Topics that are covered include:
Objectives and content
The module probability models and stochastic simulation is devoted to a study of the theory and applications of important probability models and stochastic processes. Applications are studied analytically, by means of the techniques of mathematical statistics, and are also illustrated by means of stochastic computer simulation. The broad aim of the module is to make students aware of the following important concepts:
The specific outcomes of the module are related to the specific topics that receive attention. These topics include the following: Methods for generating random variables from distributions; Monte Carlo integration; Markov chains (including applications to Metropolis-Hastings and Gibbs sampler methods); Homogeneous and non-homogeneous Poisson processes; Markov processes; variance reduction techniques in stochastic simulation.
The final honours grading will be the weighted average of the marks obtained for each module (based on module credits) as follows:
Students are expected to pass (i.e. with a mark of not less than 50%) modules totalling at least 120 credits (as outlined above).
N.B. There are no rewrite/supplementary examinations for students failing modules.
Credit may be awarded for at most one (narrowly) failed module (to a maximum of 60 credits) in respect of equivalent subjects which students subsequently pass through the Actuarial Society of South Africa.
The calculation of the final mark for each module may differ by module, but for the Actuarial Science modules it will typically be an average of the class mark (based on all relevant tests and assignments) and examination mark. For the Actuarial Science modules students will be required to have a class mark of at least 45% (based on class tests and possibly other hand-in work as specified) in order to be given entry to the final examination for that module.
Against a global backdrop of increasing demand for specialists in Quantitative Finance and Financial Risk Management, a Masters degree remains highly sought after as a differentiating factor.
Stellenbosch University offers both a Masters by coursework – aimed at enhancing sought after specialized skills across a wide-range of subject material. In addition, we offer a Masters by dissertation – for more focussed and specialized candidates.
A BComHons in Financial Risk Management from Stellenbosch University or an equivalent qualification from another recognised university.
The number of students selected can be influenced by, for example, staff capacity and the availability of resources within the Department, as well as academic merit and University transformation objectives. As staff capacity and resources can fluctuate from year to year, the number of students selected can also differ from year to year.
If the Financial Risk Management or Mathematical Statistics background of the applicant is deemed insufficient after a case-by-case determination by the Department of Statistics and Actuarial Science, the Department may require an additional departmental assessment on Financial Risk Management or Mathematical Statistics topics. Students may also be required to complete additional Stellenbosch University modules along with their MCom studies.
You can choose between two possible options:
See the Faculty Calendar (Yearbook) here for a summary description of the various Masters modules.
| Module | Code | Semester | Credits |
|---|---|---|---|
| Extreme Value Theory A | 10441-813 | 1 | 15 |
| Extreme Value Theory B | 10442-843 | 2 | 15 |
| Advanced Financial Risk Management A** | 10501-831 | 1 | 15 |
| Advanced Financial Risk Management B* | 10503-861 | 2 | 15 |
| Advanced Financial Risk Programming | 10504-835 | 1 | 15 |
| Advanced Portfolio Management Theory A | 10517-833 | 2 | 15 |
| Advanced Portfolio Management Theory B (VaR) | 10518-863 | 1 | 15 |
| Credit Derivative Instruments A | 10575-834 | 2 | 15 |
| Credit Derivative Instruments B | 10576-864 | NA | 15 |
| Thesis: Financial Risk Management Compulsory with 879 | 11237-891 / -892 | 1 & 2 | 90 / 120 |
| Research Assignment: Financial Risk Management Compulsory with 879 | 11218-893 | 1 & 2 | 60 |
| Elective Modules | Code | Semester | Credits |
|---|---|---|---|
| These have to be selected taking into account the modules from Computer Science | |||
| Bayesian Statistics | 10394-711 | NA | 12 |
| Multivariate Statistical Analysis A | 10602-715 | 1 | 12 |
| Multivariate Statistical Analysis B* | 10603-745 | 2 | 12 |
| Stochastic Simulation | 65250-718 | 2 | 12 |
| Time Series Analysis | 10751-747 | 1 | 12 |
NA – This module is not presented in 2023.
* Statistical Learning Theory (Honours module) – see content at Honours Mathematical Statistics
** Alternative Investments (new content)