Associate Professor of Quantitative Finance (Practice)
Academic Director, Msc in Quantitative Finance
Lee Kong Chian School of Business
Singapore Management University
The MQF curriculum bridges theory and practice by helping you build up expertise and skillsets that you can immediately apply to solve real world problems.
Do you have a passion to learn how to apply quantitative and computational skills to tackle practical problems in pricing, hedging, risk management, algorithmic trading, portfolio management, and other cutting edge financial applications? To succeed as a financial professional in today’s competitive market, you need to be well versed in financial knowledge, quantitative aptitude, and programming skill. The Master of Science in Quantitative Finance (MQF) programme offers a well-designed suite of courses to enhance your understanding in all these areas. At SMU, we pride ourselves on taking a practitioner-oriented teaching pedagogy. The MQF curriculum bridges theory and practice by helping you build up expertise and skillsets that you can immediately apply to solve real world problems. As an example, our programme is unique in Singapore in that we utilise a quantitative trading lab equipped with industry-grade trading software to facilitate learning of quantitative trading algorithms and market microstructure.
Join us now and discover the exciting career opportunities available to you with a Master of Science in Quantitative Finance from SMU!
The teaching team comprises of SMU and Cass Business School faculty members, as well as industry professionals.
Some of the professors teaching in the current cohort are:
Associate Professor of Quantitative Finance (Practice)
Academic Director, Msc in Quantitative Finance
PhD, University of Cambridge
Associate Professor of Quantitative Finance (Practice)
PhD, National University of Singapore
OUB Professorial Chair and Professor of Finance
PhD, Stanford University
Associate Professor of Quantitative Finance (Education)
Area Coordinator, Quantitative Finance
PhD, University of Auckland
PhD in Finance, (University of Pennsylvania)
AB in Economics & ScB in Physics, (Brown University)
Senior Lecturer of Quantitative Finance
PhD, National University of Singapore
Head of Market Risk Model Validation, Maybank
PhD, Kyoto University
Head of Quantitative Research, VAR Strategies
PhD, Tilburg University
MPhil in Statistics, University of Cambridge
Founder, Lucent Alpha Fund
PhD, Duke University
Founder & CEO, Noviscient
PhD, EDHEC Business School
CIO, UOB Private Bank
PhD, EDHEC Risk Institute
Head, MRM Analytics & Market Price Control, OCBC
PhD, University of Wuppertal
Head, Market Risk Analytics, OCBC Bank
PhD, Imperial College, London
Portfolio Manager, WorldQuant
PhD, University of Cambridge
Portfolio Manager at GIC
PhD, National University of Singapore
Head of Investment Solutions, UOB Asset Management
PhD, University of Tokyo
Professor of Finance & Econometrics
PhD, Oxford University
Senior Lecturer in Actuarial Science
PhD, Finance, Bayes, City
Reader in Mathematical Finance
PhD, Finance, Warwick Business School
The SMU MSc in Quantitative Finance is designed to equip students with both an in-depth knowledge of quantitative finance and a business-oriented, entrepreneurial mindset. As such, the curriculum will bridge both theory and practice, helping students to build up expertise and skillsets which can be immediately applied to solving real world problems.
The curriculum consists of 9 core modules and 4 elective modules. Students have the option to specialise in either one of the elective tracks.
This course introduces students to the basic concepts used for pricing and analysing financial securities centered on spot markets. The efficiency of financial markets is discussed together with the question of whether stock prices are predictable. The importance of the risk and its trade off with return will be analysed in depth. While academically rigorous in outlining theoretical models, the course also focuses on practical applications and discusses empirical findings.
This course aims to equip students with the fundamentals of stochastic calculus and their applications in pricing and hedging of financial derivatives. The topics covered include probability theory, and the basic techniques and tools of stochastic calculus which are pertinent to the risk-neutral pricing framework. Students will build up aptitude in handling stochastic differential equations, and using them to model financial instruments. The course is an interesting mix of mathematics and financial applications - students will be able to appreciate how elementary financial concepts like the no-arbitrage principle, coupled with careful mathematical reasoning, can lead to a sophisticated framework for pricing, hedging, and risk management.
This course aims to build a foundation for students to grasp the significance and implications of events in financial markets by analysing data from a quantitative finance or alpha-seeking perspective. The topics covered include relevant econometrical and other statistical procedures. Procedural algorithms will be examined in detail through hands-on programming. The computing skills and quantitative finance knowledge and insights gained will heighten analytical competency and awareness of global financial market conditions.
This course is aimed at students with programming background and who have studied some financial models, but not know how to put the two together. The objective is to teach students not just how to implement models, but more importantly how to think in a structured way. Computational framework also cultivates a logical and systematic thinking process, providing a rigorous framework to tackle and solve real world problems.
The course aims to give students a better understanding of derivatives and derivatives markets, and introduces them to the valuations and uses of derivatives such as forwards, futures, options and swaps. As derivatives markets grow in importance, evidenced by the increase in financial derivatives being used for hedging and risk management, holistic understanding of derivatives has become more crucial for modern financial practitioners.
This course acquaints students with the main modelling streams in fixed income securities and enables them to use models in this area in practical applications. Fundamental mathematical modelling techniques will also be taught. Since the financial crisis, the fixed income market has deviated drastically from the standard textbook settings. This course is designed to bring students up to speed with the state-of-the-art quantitative modelling in this domain. Both linear and nonlinear products will be covered, with a strong emphasis on efficient pricing, valuation, and risk management.
The aim of this module is to develop a solid background for evaluating, managing, and researching financial risk. To this end, students will learn to analyse and quantify risk according to current best practice in the markets. This provides students with an in-depth understanding of the types of risks a financial institution faces in its day-to-day operation. The latest developments, including BASEL regulatory framework, RiskMetrics, and CreditMetrics methodologies will also be covered.
This course teaches computing concepts such as program structure, i/o handling, data types, arrays, expressions, control statements, and data structures in parallel with, and applied to numerical methods such as root finding, non-linear equations, linear systems, interpolation, extrapolation, differentiation, integration, and random number generation techniques. Emphasis will be placed on the numerical concepts particularly applicable in QF, so that students can build a strong theoretical foundation in basic numerical methods and develop the ability to implement them independently. Students will also learn how to generate stochastic processes and Brownian motions, to perform Monte Carlo simulations, to construct lattice models, and to price American options numerically. Further applications in risk management will also be covered.
This course provides detailed knowledge and understanding of the essential technical tools required to carry out advanced econometric research such as fractional integration and long memory processes. Students will gain insight into the implications of financial theories and the practical aspects of real-world modelling.
To satisfy the requirements of this master’s degree programme, students must successfully complete nine core courses and four electives. The programme duration for both the SMU's MQF local track and international track are both 12-months, while the part-time track is a 24-months programme. Students may choose to specialise in one of the following elective tracks: Financial Data Science, Algorithmic Trading, and Risk Analytics.
The mission of the MQF programme is to produce high-quality professionals who will be highly sought after by global and regional banks, quantitative hedge funds, asset management companies, as well as regulators in Singapore.
As the global financial industry is getting more sophisticated in risk management, product innovation, and proprietary trading, the demand for quantitative finance professionals is on the rise.
Our MQF programme is positioned to flourish with this rising trend.
The Master of Science in Quantitative Finance (MQF) by coursework is a full-time programme to be completed within 12 months. It caters to a need identified by the Monetary Authority of Singapore (MAS) - to groom a critical mass of specialists in quantitative finance, which is the objective underlying the second phase of MAS's Finance Scholarship Programme.
Participants can pursue their master studies in two ways: (1) The Singapore-Based Track, or (2) The International Track.
For both tracks, the same core courses and programme fees apply.
Participants will take all their courses at SMU (Singapore) and obtain an SMU MQF Degree.
This is the preferred path for those who are already working in Singapore or who are unable to spend four months in London at the international track. On top of being Association to Advance Collegiate Schools of Business (AACSB) and European for Management Development Quality Improvement System (EQUIS) accredited, the Lee Kong Chian School of Business is also highly ranked for its research productivity, teaching effectiveness, graduate employability and international reputation.
Participants will take their Term 1 and Term 4 courses at SMU (Singapore), and Term 2 and Term 3 courses at Bayes Business School (London, UK), and graduate with a Joint MQF degree from both institutions.
