Postgraduate Courses

This course will survey modern financial technology, through the lens of statistics, which is the science of the analysis of data. Students will learn how statistical methodology, in conjunction with advances in technology, is used to efficiently acquire, utilize and interpret data, as it relates to innovations in the financial services sector. This course will develop skillsets for Big Data analytics and Predictive modelling, for better understanding of the financial markets.

The course will give students a deeper understanding of the foundations of probability theory, such as probability theory from a measure-theoretic perspective, convergences of distributions and probability measures, and conditional expectations. During the course, important theorems, such as Radon-Nikodym theorem, Fubini theorem, and general central limit theorems, will be investigated.

The objective of this course is to provide students with optimization theory and concepts. Main topics cover linear optimization, simplex method, duality theory, convex analysis, and dynamic programming. The emphasis will be on methodology, modelling techniques and mathematical insights.

The objective of this course is to provide students with an extensive exposure to important research in financial technology and a rigorous training in related research methodologies. Main topics include cryptocurrencies, blockchain, P2P lending, crowdfunding, robo-advisors, regulatory technology (RegTech), and insurance technology (InsurTech). This course also enables students to gain an appreciation for how research in financial technologies improves traditional financial services and overcomes various difficulties inherent in the current financial system.

This course covers the fundamentals of machine learning and artificial intelligence, and their applications in computer vision, image processing, natural language processing, and robotics. The topics include major learning paradigms (supervised learning, unsupervised learning and reinforcement learning), learning models (such as neural networks, Bayesian classification, clustering, kernels, feature extraction), and other problem solving techniques (such as heuristic search, constraint satisfaction solvers and knowledge-based systems) in AI.

The objective of this course is to provide students with fundamentals of stochastic processes. Main topics cover Poisson processes, renewal theory, discrete-time Markov chains, continuous-time Markov chains, and martingales. The emphasis will be on methodologies, fundamental concepts, and mathematical insights.

This is a graduate level course in stochastic calculus for MPhil/PhD students in Financial Technology and other related fields. This course aims to provide a rigorous mathematical introduction to the tools of stochastic calculus used in derivative pricing and financial modeling. Topics include Brownian motion, stopping times, stochastic integral, Itô’s formula, stochastic differential equations, martingales, Girsanov’s theorem, option pricing, etc.

This course introduces the main issues in corporate finance, identifies principal theoretical tools and empirical approaches, and fosters thinking about current research questions. The theoretical part includes classic theories such as Modigliani‐Miller theorem, Coase theorem, and Fisher separation theorem, with a focus on financing decisions of firms, corporate governance, and their implications. The empirical part reviews econometric methods commonly used in corporate finance research and covers selected topics.

This is a course in graduate level microeconomic theory for PhD students in financial technology and other related fields. This course covers topics including consumer theory, producer theory, uncertainty, general equilibrium,and matching. The required background knowledge for the course are intermediate microeconomic theory and mathematics through calculus of several variables and introductory real analysis. Additional mathematical tools will be explained briefly as the course proceeds. This course serves as the first rigorous training in economics and finance and helps lay down a solid foundation in economic modelling for future research.

This course addresses issues in both theoretical development and empirical studies of asset pricing. The theoretical part covers portfolio theory, arbitrage pricing theory with large numbers of assets, the intertemporal asset pricing model and the production-based asset pricing model. Topics related to derivative pricing are also covered. The empirical part covers asset return predictability, volatility-return relationship, asset pricing testing methodology, popular factor models used by practitioners and empirical findings in derivative markets.

This course provides an introduction to textual analysis in social science research. It covers text mining models and related statistical tools, including Dictionary Method, SVD, Word2Vec, WEAT, Probabilistic Modeling, Regression Modeling, and Large Language Models in modern social science research. Applications related to the financial market are emphasized, including macro-finance, empirical asset pricing, empirical corporate finance, and ESG.

This course covers basic pricing theory of financial derivatives and risk hedging of exotic options. The course starts with the fundamental theorem of asset pricing and risk neutral valuation principle. The renowned Black-Scholes pricing theory and martingale pricing theory are introduced. Advanced topics include exchange options, quanto options, implied volatility and VIX.

This course covers Monte Carol simulation methods from the perspectives of derivatives pricing, credit risk modeling and trading strategies. The first topic starts with various sampling methods for generating random variables, like the basic inverse transform method and acceptance-rejection method. Special emphasis is placed on simulation of normal distributions. Next, we consider pricing financial derivatives via simulation. The dynamic price processes include the Geometric Brownian motion and jump diffusion models. Various variance reduction techniques, like the antithetic variate, control variate, conditioning and stratified sampling are considered. The solution of the optimal stopping model of an American option via the Longstaff-Schwartz regression method is discussed. We also consider rare event simulation via various importance sampling methods, like the mean drift method and cross entropy method. Applications in risk measures calculation in credit risk models, like the Gaussian copula models, are considered.

This course introduces various novel types of financial instruments enabled by blockchains, collectively known as Decentralized Finance (DeFi). Students will delve into key DeFi elements, gaining both theoretical knowledge and practical skills to effectively navigate and engage with DeFi platforms and protocols.

This course provides an opportunity for students to develop and apply FinTech research in an industrial organization. Students will work in a designated organization conducting FinTech research-related work under the supervision of their supervisors. Graded P or F.

Advanced seminar series presented by guest speakers and faculty members on selected topics in Financial Technology. This course is offered every regular term. Graded P or F.

Independent study in a designated subject under direct guidance of a faculty member to provide students the advanced knowledge and research skill sets on a topic of Financial Technology. Required readings, tutorial discussions, and submission of report(s) will be used for assessment. The course may be repeated for credit if different topics are studied. Graded P or F.

This course examines areas of current interest and special topics in financial technology. It employs a mix of lectures, case studies, and projects. Topics may vary from year to year and will be announced at the beginning of each term. This course may be graded by letter or P/F for different offerings.

Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.