Master of Financial Engineering

Purpose

The primary purpose of the Master of Financial Engineering is to develop and extend the knowledge of learners in the field of financial engineering to an advanced level so that they are prepared for sophisticated and specialised professional employment. Learners will be trained to master the technical aspects of modern financial engineering through high-level theoretical engagement with the subject matter, coupled with the ability to relate intellectual knowledge to different, interconnected, and practical contexts.

The qualification will provide learners with a structured and sustained learning opportunity at the cutting edge of global knowledge and experience, with abundant opportunities for research related to effective approaches and practices of financial engineering.

Upon completion of the qualification, qualifying learners will be able to do the following

• Apply advanced theoretical and practical macro vision, considering socio-political and multi-cultural factors of financial economics internationally, nationally, provincially/ regionally, and locally.
• Analyse and apply all aspects of financial markets, their regulation, and the employed strategies.
• Combine practical financial market knowledge and technical mathematical, statistical, and computing knowledge to model, quantify, and combine risk.
• Analyse financial market data from various sources, make sound judgements, and propose solutions and/or applications.
• Address complex financial engineering problems and challenges rigorously, systematically, and creatively.
• Apply critical thinking and problem-solving abilities, considering the business environment, products, technical aspects, and the external market and regulatory context.
• Deploy complex financial mathematical ideas/models/systems to business users in an understandable and accessible medium and format.
• Apply interdisciplinary knowledge and research skills to practical business and industry problems.

Rationale

Financial engineering is a relatively new quantitative finance discipline, the development of which emerged during the 1970s after the seminal publication of Black and Scholes in 1973. The key insight in the Black- Scholes modelling approach, based on the mathematical notion of trading in continuous-time and the economic notion of arbitrage, reveals how primary or "linear" financial instruments may be combined to create new "non-linear" financial products, called derivatives that may be utilised for the mitigation of financial risk.

The early 1980s revealed a mathematical formalism of this approach, which has since evolved into a well-established distinct applied branch of mathematics commonly referred to in academia as the "mathematics of arbitrage". This has had a profound effect on financial markets, leading to a paradigm shift in understanding and managing financial risks with the use of sophisticated mathematical models. The ensuing decades have produced a multi-trillion-dollar derivatives industry. The rigorous mathematical construction of financial derivatives from simpler instruments has coined the now globally understood moniker "financial engineering". After the global financial crisis of 2008, the international demand for Quants has increased exponentially.

Quantitative Analysts (Quants) are now found in all technically sophisticated areas of corporate, retail and investment banking, insurance, asset management, and treasury advisory services. Both engineering new products and reverse-engineering existing products is a daily exercise in financial services. It is the role of the Quant to assemble and dissemble these risks, and to accurately price, combine and risk-manage them. Moreover, Quants are in increasing demand due to their natural ability to complement their skill set with knowledge of Artificial Intelligence and Machine Learning, the applications of which are on the rise.

The institution conducted a Financial Services Sector Assessment in 2013. This assessment involved interviews and surveys with companies across the Financial Services Sector in Banking, Insurance, Advisory Services and Asset Management, and included Regulators and Industry Bodies. It encompassed several areas including skills deficits, recruitment, work environments, training and development, and tertiary education. The study concluded that four-year undergraduate degrees do not adequately prepare learners for specialist roles in Financial Services. Companies asserted that there exists the need for Master's qualifications in Risk Management and Financial Engineering specifically designed to meet the unique needs of the sector.

A qualified financial engineer should master and integrate advanced concepts in programming, probability, statistics, mathematical modelling, financial markets, regulation, risk management and stochastics. qualified learners will be astute learners of the markets, familiar with the technical and practical characteristics of the vast array of modern financial instruments and market mechanisms. Qualified learners will also be well versed in both standard and innovative models that are commonly used in global and South African financial services companies. Financial engineering satisfies an integral societal need by facilitating innovative solutions in an ever-changing economic environment while adhering to a special code of conduct.

Recognition of Prior Learning (RPL)

The institution has an approved Recognition of Prior Learning (RPL) policy which is applicable to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications. RPL may be applied for access, credits from modules and credits for or towards the qualification.

RPL for access

• Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.
• To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal and/or informal learning to cope with the qualification expectations should they be allowed entrance into the qualification.

RPL for exemption of modules

• Learners may apply for RPL to be exempted from modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

RPL for credit

• Learners may also apply for RPL for credit for or towards the qualification, in which they must provide evidence in the form of a portfolio that demonstrates prior learning through formal, non-formal and/or informal learning to obtain credits towards the qualification.
• Credit shall be appropriate to the context in which it is awarded and accepted.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Arts Honours in Monetary and Financial Economics, NQF Level 08.

Or

• Bachelor of Science Honours in Financial Engineering, NQF Level 08.

Or

• A relevant Postgraduate Diploma in the related field, NQF Level 08.

This qualification consists of the following compulsory and elective modules at National Qualifications Framework Level 9 totalling 180 Credits.

Compulsory Modules, Level 9, 165 Credits

• Financial Instruments, Risk and Regulation, 30 Credits.
• Computational Finance 1, 15 Credits.
• Computational Finance 2, 30 Credits.
• Stochastic Financial Modelling 1, 30 Credits.
• Stochastic Financial Modelling 2, 15 Credits.
• Financial Engineering Research, 45 Credits.

Elective Modules, Level 9, 15 Credits (Select one module)

• Data Science for Industry, 15 Credits.
• Financial Systems Design,15 Credits.

1.Demonstrate the ability to reason about probabilistic problems, and compute solutions using Lebesgue integrals, functional analysis, and conditional expectations.

1. Display a practical understanding of all aspects of financial markets, their regulation and the strategies employed.
2. Demonstrate the ability to combine practical financial market knowledge and technical mathematical, statistical, and computing knowledge to model, quantify, and combine risk.
3. Analyse financial market data from various sources, make sound judgements, and propose solutions and/or applications.
4. Address complex financial engineering problems and challenges rigorously, systematically, and creatively.
5. Demonstrate critical thinking and problem-solving abilities, considering the business environment, products, technical aspects, and the external market and regulatory context.
6. Demonstrate the ability to deploy complex financial mathematical ideas, models, and systems to business users in an understandable and accessible medium and format.
7. Apply interdisciplinary knowledge and research skills to practical business and industry problems.

Associated Assessment Criteria for Exit Level Outcome 1

• Evaluate probabilistic problems, and compute solutions using Lebesgue integrals, functional analysis, and conditional expectations.
• Apply stochastic processes and martingales in discrete and continuous time.
• Compute stochastic integrals and apply solutions to stochastic differential equations.

Associated Assessment Criteria for Exit Level Outcome 2

• Explore the functions of global and South African financial markets within the local and global economy.
• Apply financial product/instrument creation using static and dynamic replication strategies under the principle of no arbitrage.
• Devise appropriate collateral strategies and credit support annexes to mitigate counterparty credit risk on traded OTC financial products.
• Apply basic measure theory, as the framework for a mathematical treatment of probability theory.
• Explain basic concepts in probability theory: sample spaces, random variables, expectations, independence, and conditioning.
• Apply convergence of random variables and stochastic limit theorems.
• Apply basic knowledge of standard financial theories and models.

Associated Assessment Criteria for Exit Level Outcome 3

• Display knowledge about the univariate and multivariate probability distributions common in financial risk management.
• Understand fundamental financial concepts and be able to apply these fundamental concepts in a variety of circumstances.
• Adjust risk-neutral values of financial products/instruments for idiosyncratic credit and funding-related costs.
• Implement basic empirical techniques to aspects of standard financial models.
• Explain the mechanics of a change of measure/numeraire pair and use it to compute the prices of various financial instruments.
• Display an understanding of various approaches to modelling volatility in financial markets, including local volatility and stochastic volatility models.

Associated Assessment Criteria for Exit Level Outcome 4

• Measure, analyse and manage market and credit risks on linear and non-linear financial products.
• Use Monte Carlo Integration techniques in conjunction with risk-neutral pricing to value financial instruments.
• Obtain practical knowledge of market conventions associated with commonly traded over-the-counter (OTC) and exchange-traded financial products/instruments.
• Explain how martingale methods are used in finance to price and hedge financial instruments, in discrete and continuous time.
• Implement standardised and internal model approaches to calculate regulatory capital on linear and non-linear financial products and assess the efficacy of these via profit and loss attribution and back-testing.
• Produce basic Python scripts that implement mathematical algorithms and develop innovative financial software.

Associated Assessment Criteria for Exit Level Outcome 5

• Apply the Basel Accords, in relation to market and credit risks, and understand the prudential need and impact of regulatory capital on the viability of bank sales and trading activities.
• Use numerical techniques to generate and analyse Pseudo-Random Numbers.
• Generate stock prices and paths, using closed-form and Ito-Taylor expansions.
• Analyse the accuracy of a result and improve upon it using variance reduction techniques.
• Compute risk sensitivities for the instruments priced and be able to use these to create hedge portfolios.
• Use advanced Quasi-Monte Carlo methods and least-squares Monte Carlo methods for techniques requiring conditional expectations, such as pricing American Options.
• Use Finite Difference techniques and Fourier methods to price contingent claims.
• Compute basic valuation adjustments (XVAs) and implement local and stochastic volatility models.

Associated Assessment Criteria for Exit Level Outcome 6

• Exhibit an understanding of matrix calculus, and use it to solve problems related to interpolation, linear regression and Kalman filtering.
• Implement spectral decompositions of matrices to solve problems in numerical linear algebra and principal components analysis.
• Apply practical optimisation algorithms to such problems as non-linear regression, portfolio optimisation and calibration.

Associated Assessment Criteria for Exit Level Outcome 7

• Understand and implement structural and reduced-form credit models.
• Use generating- and characteristic functions for probabilistic computations.
• Construct pricing and valuation models for various non-linear products and instruments using model-dependent risk-neutral valuation.
• Build pricing and valuation models for various linear (bonds, swaps, futures and forward) products/instruments using model-independent risk-neutral valuation.
• Price interest rate claims using advanced interest rate models.
• Perform basic parameter estimation and model calibration.
• Exhibit an understanding of various approaches to modelling the term structure of interest rates, including short rate models and forward rate models.
• Compute prices of European options via the Fourier transform for various models of underlying asset dynamics.

Associated Assessment Criteria for Exit Level Outcome 8

• Manipulate and compute Fourier series and transforms.
• Communicate technical results coherently and accurately and produce work of creditable literary quality.

INTEGRATED ASSESSMENT

Integrated Assessment in the qualification provides an opportunity for learners to show that they can integrate concepts, ideas, and actions across this qualification to achieve competence that is grounded and coherent with the purpose of this qualification. Integrated assessment will show how already demonstrated competence in individual areas can be linked and applied for the achievement of a holistic outcome as described in the Exit Level Outcomes.

Integrated Assessment will judge the quality of the observable performance, and the quality of the reasoning that lies behind it. Assessments tools will encourage learners to give an account of the thinking and decision-making that underpin their demonstrated performance. Assessment of learners is through formative and summative assessment.

Formative Assessment

Financial Instruments, Risk and Regulation will be assessed through a sequence of practical assignments and theoretical/computational tests, which will contribute 40% to the final grade.

Summative Assessment

The examination assessment, which will contribute 60% to the final grade, will be carried out in two parts

• A theoretical written examination that tests the learner's understanding of fundamental theory.
• A practical computational examination that requires the learner to apply theoretical concepts to solve practical financial market problems. The learners will have to demonstrate computational and technical proficiency.
• Coursework counts 50% and the exam counts 50% of the final mark.
• The weighting for Financial Systems Design is coursework is weighted 40% and an exam will count 60% toward the final grade.

External examiners will be appointed from international and local universities, based on the required area of expertise, and according to standard practices within the Faculty of Commerce.