Programme Curriculum

Asset Pricing

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.

Stochastic Modelling in Finance

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.

Quantitative Analysis of Financial Markets

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.

Programming and Computation Financial

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.

Derivatives

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.

Fixed Income Securities

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.

Risk Analysis

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.

Numerical Methods

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.

Econometrics of Financial Markets

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.

Financial Data Science

1. Quantitative Trading Strategies
2. C++ for Financial Engineering
3. Financial Data Science
4. Machine Learning & Financial Application

Algorithmic Trading

1. Quantitative Trading Strategies
2. Portfolio Management
3. C++ for Financial Engineering
4. Hedge Funds

Risk Analytics

1. Credit Risk Models
2. Portfolio Management
3. Financial Data Science
4. Machine Learning & Financial Application