STAT5020 Topics in Multivariate Analysis
This is an advanced course on multivariate analysis. Topics may include: Multivariate central theorem, and its applications, factor analysis, structural equation models, and latent variable models.

STAT5030 Linear Models
This course introduces important and fundamental elements related to the area of linear statistical models. A brief review of linear algebra will be given to the students. The major substance of this course covers: 1) distribution theory: multivariate normal and related distributions, distribution of quadratic forms; 2) full-rank linear models: least squares estimation, maximum likelihood estimation, simultaneous confidence intervals, tests of linear hypotheses, generalized least squares; 3) non-full-rank linear models: estimability, parameter estimation, testable hypotheses, estimability conditions; and 4) applications of linear models: regression analysis, analysis of variance, analysis of covariance.

STAT5040 Studies on Selected Topics I
Recent topics on multivariate statistical techniques are selected for discussion. (For MPhil & PhD students in Statistics)

STAT5050 Advanced Statistical Computing
This course covers the theory and application of advanced statistical computer algorithms for solving analytically intractable problems. Typical problems include root finding, numerical integration, optimization, model selection, density estimation, and variance estimation. Specific algorithms discussed may include Newton-Raphson, Monte Carlo integration, EM, importance sampling, Markov chain Monte Carlo algorithms, simulated annealing, and bootstrap. Application fields may include bioinformatics, econometrics, and social science. (For MPhil & PhD students in Statistics)

STAT5060 Advanced Modeling and Data Analysis
This course covers recent developments in statistical modeling and data analysis. Topics may include generalized linear models (GLM), mixed effects models, hierarchical models, mixture models, generalized additive models, hidden Markov model, Bayesian network, and other advanced statistical models. Statistical analysis for different types of data, such as discrete data, non-normal continuous data, hierarchical/heterogeneous data, longitudinal data, and incomplete data, will be discussed. (For MPhil & PhD students in Statistics)

STAT5101 Foundations of Data Science
This course introduces the statistical reasoning powers in contemporary data science and the use of applied statistical methodologies as a comprehensive approach in data analysis. It provides students with the foundation knowledge to further apprehend in-depth material presented in other courses of the program. Topics include exploratory data analysis, statistical graphics, basic concept in probability, discrete/continuous probability distribution, point and confidence interval estimation, hypothesis testing for one/two sample. The effective use of desktop productivity tools, such as R and Microsoft Excel, in the workplace environment for collecting and analysing corporate data with aforementioned fundamental statistical concepts will be emphasized. (For MSc students in Data Science & Business Statistics)

STAT5102 Regression in Practice
This course introduces applied regression methodologies using various functional areas of business as the frame of reference, including management, finance and marketing. Topics include the use of correlation coefficient as a measure of relationship, the use of simple linear regression, multiple regression and logistic regression in business projection and forecasting, as well as the use of model building techniques to incorporate qualitative variables in prediction. (For MSc students in Data Science & Business Statistics)

STAT5103 High-dimensional Data Analysis
Beginning with an introduction to the basic knowledge in multivariate and high-dimensional data analysis, including multinormal distribution, descriptive statistics, and graphical displays, this course focuses on the dimensionality reduction methods, which are commonly used in high-dimensional data analysis. Selected topics include principal component analysis, factor analysis and canonical correlation analysis. (For MSc students in Data Science & Business Statistics; For MSc students in Risk Management Science)

STAT5104 Data Mining
Data Mining is a relatively new subject focusing on data collection, storing and automatic algorithms for finding patterns and relations in data. Because of the cheap computing power, the applications of data mining techniques are getting surprisingly board, including policymaking, business decision-making, marketing and stock trading. In this course we introduce the basic ideas and techniques of data mining. The students shall have hands on experience with interesting data sets and learn how to use some of the publicly available software to do data mining with their own data sets. (For MSc students in Data Science & Business Statistics)

STAT5105 Applied Survival Data Analysis
This course deals with the analysis of time-to-event (survival or failure-time) data, which are commonly encountered in scientific investigations and risk management. It is being extensively used in clinical trials, biological and epidemiological studies, engineering, finance and social sciences. This course provides an opportunity for students to learn statistical lifetime probability distributions that are useful for modeling time-to-event data. The primary focus of the course is on the statistical methods designed for extraction of information from time-to-event data. This course introduces statistical theory and methodology for the analysis of time-to-event data from complete and censored samples with emphasizes on statistical lifetime distributions, types of censoring, graphical techniques, nonparametric/ parametric estimation, lifetime regression models and related topics. (For MSc students in Data Science & Business Statistics; For MSc students in Risk Management Science)

STAT5106 Programming Techniques for Data Science
This course teaches programming fundamentals for data scientists. Students will learn programming techniques with emphasis on data source connection, data pre-processing pipeline, exploratory data analysis, data visualizations and data reporting. Topics include basic concepts for programming, lists, objects, dictionaries and functions, matrix and data frame, use of programming packages and libraries, database manipulation, API connection and web scraping, descriptive statistics, simulation and Monte Carlo methods, and statistical graphics.

STAT5107 Discrete Data Analytics
This course provides a practically oriented treatment of modern methods for the analysis of categorical data. Topics include analysis of two-way contingency tables, logistic regression, log-linear model, generalized linear model, classification and regression tree method.

STAT6040 Studies on Selected Topics II
Recent topics on computer-intensive statistical method are selected for discussion. (For MPhil & PhD students in Statistics)

STAT6050 Studies on Selected Topics III
Recent topics on statistical modelling are selected for discussion. (For MPhil & PhD students in Statistics)

STAT6060 Studies on Selected Topics IV
Recent topics on statistical modelling are selected for discussion. (For MPhil & PhD students in Statistics)

STAT6104 Financial Time Series
This course deals with the methodology and applications of business and financial time series. Topics include statistical tools useful in analysing time series, models for stationary and non-stationary time series, seasonality, forecasting techniques, heteroskedasticity, ARCH and GARCH models, and multivariate time series. (For MSc students in Data Science & Business Statistics; For MSc students in Risk Management Science)

STAT6105 Basic Actuarial Principles and Their Applications
This course introduces the basic actuarial principles applicable to a variety of financial security systems. Focus will be on topics related to life insurances and annuities. It also develops students’ understanding of the purpose of these systems, and the design and development of financial security products. Topics include theory of interest, survival distribution and life tables, life insurance, life annuities, and benefit premiums. (For MSc students in Data Science & Business Statistics; For MSc students in Risk Management Science)

STAT6106 Applied Bayesian Methods
This course is an introduction to practical Bayesian methodology. The use of conjugate families is discussed. Building on techniques in Statistical Computing, methods for calculating posterior distributions are presented, as is the concept of hierarchical model. The emphasis throughout is on the application of Bayesian thinking to problems in data analysis. (For MSc students in Data Science & Business Statistics)

STAT6107 Selected Topics on Data Science and Business Statistics
Recent topics on data science and business statistics are selected for discussion. (For MSc students in Data Science & Business Statistics; For MSc students in Risk Management Science)

STAT6108 Official Statistics and Structural Equation Modelling
The course introduces the basic principles, concepts and methodologies of official statistics and business statistics. The course is divided into two parts, “Official Statistics” and “Structural Equation Modelling”. (For MSc students in Data Science & Business Statistics; For MSc students in Risk Management Science)

STAT8003, 8006, 8012 Thesis Research
In this course, a student is required to meet with his/her supervisor regularly who provides necessary guidance and supervision to write up a thesis and monitors the student’s academic progress.

RMSC4001 Simulation Methods for Risk Management Science and Finance
This course starts with presenting standard topics in simulation including random variable generations, variance reduction methods and statistical analysis of simulation outputs. The course then reviews the applications of these methods to derivative security pricing. Topics addressed include: importance sampling, martingale control variables, stratification and the estimation of derivatives. Additional topics include the use of low discrepancy sequence (quasi-random numbers), pricing American options and scenario simulation for risk management. (For MPhil students in Risk Management Science)

RMSC4002 Data Analysis in Finance and Risk Management Science
This course covers modern data analysis techniques that are commonly used in financial and risk management. Topics include applications of multivariate techniques such as principal component and canonical correlation to asset management, Value-at-Risks, GARCH model in estimating volatility, time series methods in term-structure analysis, and data mining methods such as logistic regression, k-mean clustering and classification tree, and neural network. (For MPhil students in Risk Management Science)

RMSC4003 Statistical Modelling in Financial Markets
This course is designed to introduce the current developments in risk management in the financial markets. Risk management ideas associated with three general important areas in finance will be discussed: asset management, derivative pricing and fixed income models. Emphasis will be placed on the statistical modelling aspects on some of the commonly used models in these areas. (For MPhil students in Risk Management Science)

RMSC4004 Theory of Risk and Insurance
This course covers the theory of risk and its applications to insurance. Topics include: classical and stochastic risk models, ruin theory, claims modelling and evaluations, risk premium pricing, loss distributions and creditability theory. (For MPhil students in Risk Management Science)

RMSC4005 Stochastic Calculus for Finance and Risk
This course starts with the introduction of the concepts of arbitrage and risk-neutral pricing. It then proceeds to discuss the stochastic calculus foundations for continuous-time finance models. Topics include: Brownian motion, stochastic integral, Itô’s formula, Girsanov’s change of measure, and the relationship between stochastic calculus and partial differential equations. Examples will be taken from equity options, including the Black-Scholes formula for foreign exchange and term-structure models. (For MPhil students in Risk Management Science)

RMSC4007 Risk Management with Derivatives Concepts
This course aims at understanding the application of derivatives theories for the practical risk management. It starts by reviewing basic concepts of pricing and hedging derivatives, like risk-neutral valuation, arbitrage strategies, hedging strategies, implied volatilities and the Greeks. The Value-at-Risk framework for derivatives positions is discussed. Student will also learn how to apply option theoretic approach to credit risk management. Specifically, the capital structure model will be applied to measure the default probability. The Moody’s KMV methodology and CreditRisk+ are introduced. Advisory: For majors only. (For MPhil students in Risk Management Science)

RMSC5001 Advanced Statistical Theory In Risk Management
This course discusses modern applications of advanced statistical methods in finance. Methods include times series methods, stochastic process approach, data mining, and Monte Carlo simulations. (For MSc students in Risk Management Science and Data Analytics)

RMSC5002 Principles of Risk Management
This course provides students with fundamental concepts of risk and risk management. It further introduces risk management tools used in financial products. Topics include market risk, credit risk and integrated risk management. (For MSc students in Risk Management Science and Data Analytics)

RMSC5003 Risk Measures
Risk measurement and quantification are the fundamentals of risk management procedures. This course discusses various types of risk measures but mainly focuses on the methodologies of calculating Value-at-Risk (VaR) such as historical simulation, parametric VaR, delta-gamma approximation and Monte-Carlo simulation. The uses of VaR in risk management are also addressed. Topics include portfolio risk management, asset allocation and measuring the performance of portfolio managers. (For MSc students in Risk Management Science and Data Analytics; For MPhil students in Risk Management Science)

RMSC5004 Cases for Risk Management in Practice
Students need to present and discuss literatures assigned to them by the instructor on topics of current interest in financial risk management. (For MSc students in Risk Management Science and Data Analytics; For MPhil students in Risk Management Science)

RMSC5101 Statistical Methods in Risk Management and Finance
This course is designed to introduce the current developments in risk management in the financial markets. Risk management ideas associated with three general important areas in finance will be discussed: asset management, derivative pricing, and fixed income models. Emphasis will be placed on the statistical modelling aspects on some of the commonly used models in these areas. (For MSc students in Risk Management Science and Data Analytics; For MSc students in Data Science & Business Statistics)

RMSC5102 Simulation Techniques in Risk Management and Finance
This course starts with presenting standard topics in simulation including random variable generations, variance reduction methods and statistical analysis of simulation outputs. The course then reviews the applications of these methods to derivative security pricing. Topics addressed include importance sampling, martingale control variables, stratification and the estimation of derivatives. Additional topics include the use of low discrepancy sequence (quasi-random numbers), pricing American options and scenario simulation for risk management. (For MSc students in Risk Management Science and Data Analytics)

RMSC6001 Interest Rate and Fixed Incomes Risk Management
Fixed income securities are highly sensitive to the fluctuation of interest rates. Thus interest rate modeling becomes crucial for pricing and managing fixed income securities. This course introduces various types of fixed income securities and interest rate models. It covers the celebrated Heath-Jarrow-Morton (HJM) model as well as some term-structure models including Ho-Lee, Hull-White and the CIR models. (For MSc students in Risk Management Science and Data Analytics; For MSc students in Data Science & Business Statistics)

RMSC6002 Credit Risk Management
Credit risk is an important topic in the financial market in the way that over 70% of losses in the banking industry are caused by credit risk. This includes defaults of bank loans, corporate bonds and/or counter-parties. This course aims at providing students with some quantitative methods in credit risk management. Ideas of reduced-form models and structure models to credit risk are discussed. Software packages such as CreditmetricsTM and KMV methodologies are introduced. Applications of credit derivatives are also addressed. (For MSc students in Risk Management Science and Data Analytics)

RMSC6003 Operational Risk Management
Catastrophic losses are usually caused by a combination of market risk and credit risk along with failure of financial controls, which is a form of operational risk. This course introduces some tools in operational risk management. Topics include earnings volatility, casual networks actuarial models, capital allocation and regulatory requirements. (For MSc students in Risk Management Science and Data Analytics)

RMSC6004 Special Topics in Risk Management
The course aims at discussing recent advances in risk management. (For MSc students in Risk Management Science and Data Analytics; For MSc students in Data Science & Business Statistics)

RMSC6005 Special Topics in Quantitative Finance
The course aims at discussing recent advances in quantitative finance. (For MSc students in Risk Management Science and Data Analytics; For MPhil students in Risk Management Science; For MSc students in Data Science and Business Statistics)

RMSC6006 Portfolio theory with Risk Management Perspective
The course introduces the general theory of financial portfolio based on utility theory. Non-arbitrage pricing theory based on the idea of risk management will be applied. Selected topics include utility functions, risk aversion, the St Petersburg paradox, dynamic asset pricing, forecast and valuation, portfolio optimization under budget constraints, wealth consumption, and growth versus income. ((For MSc students in Risk Management Science and Data Analytics; For MPhil students in Risk Management Science)

RMSC6007 Risk and Financial Data Analytics with Python

The aim of the course is to provide students with a broad understanding of the principles and techniques of Python coding for finance applications on Jupyter notebook and/or other programming interfaces. Following an introduction to the basics of Python, the course is further divided into two main sections. The first part covers the fundamentals of data reduction and modelling applied specifically to financial data with Python libraries. Techniques for derivative pricing, risk measures estimation with Python are also discussed. The second part emphasises on several specific examples of applying state-of-the-art machine learning tools for various modelling/ analyses. Understanding the problems covered in the course will be important to students pursing a career in a rapidly expanding technology-intensive field of finance and risk management. (For MSc students in Risk Management Science and Data Analytics)

RMSC6008 Practicum
The course introduces the general theory of financial portfolio based on utility theory. Non-arbitrage pricing theory based on the idea of risk management will be applied. Selected topics include utility functions, risk aversion, the St Petersburg paradox, dynamic asset pricing, forecast and valuation, portfolio optimization under budget constraints, wealth consumption, and growth versus income. (For MSc students in Risk Management Science and Data Analytics)

RMSC8206, 8301, 8302 Research for Thesis
Students are required to conduct research under the supervision of their advisors. (For MPhil students in Risk Management Science)