Course Categories
The core graduate course in probability theory, STAT5005, and the core graduate course in the theory of statistics, STAT5010, are designed for students of advanced mathematical maturity interested in research careers in probability theory or statistics. The core graduate course in multivariate analysis and linear models, STAT 5020/5030, are designed to develop advanced data analytic skills and knowledge in linear models.
 Postgraduate Seminar Courses
A variety of topics courses and seminars are available. The content varies according to the interests of the instructor and the students. The focus ranges from exploring mathematical techniques useful to researchers in probability and statistics, to surveys of statistical methods used in particular application areas, to developing advanced data analytic skills. These courses are usually assigned with course codes of 6000level. In recent semesters, seminar topics have included: advanced Bayesian data analysis, advanced big data analytics, advanced Monte Carlo methods, statistics in computational biology/Bioinformatics, etc.
We offer two parttime taughtbased Master of Science programs. Accordingly, we offer a wide spectrum of courses during weekday evenings or Saturday afternoons. These courses are separately listed for students' convenience. Some courses are shared by both MPhil students and MSc students, such as RMSC5003 and RMSC5004.
Course List
Course Code 
Course Title 
MPhil & PhD courses 
STAT5005 
Advanced Probability Theory 
STAT5010 
Advanced Statistical Inference 
STAT5020 
Topics in Multivariate Analysis 
STAT5030 
Linear Models 
STAT5040 
Studies on Selected Topics I 
STAT5050 
Advanced Statistical Computing 
STAT5060 
Advanced Modeling and Data Analysis 
STAT6040 
Studies on Selected Topics II 
STAT6050 
Studies on Selected Topics III 
STAT6060 
Studies on Selected Topics IV 
STAT8003 
Thesis Research 
STAT8006 
Thesis Research 
STAT8012 
Thesis Research 

RMSC4001 
Simulation Methods for Risk Management Science and Finance 
RMSC4002 
Data Analysis in Finance and Risk Management Science 
RMSC4003 
Statistical Modelling in Financial Markets 
RMSC4004 
Theory of Risk and Insurance 
RMSC4005 
Stochastic Calculus for Finance and Risk 
RMSC4007 
Risk Management with Derivatives Concepts 
RMSC5003 
Risk Measures 
RMSC5004 
Cases for Risk Management in Practice 
RMSC8206 
Thesis Research 
RMSC8301 
Thesis Research 
RMSC8302 
Thesis Research 
MSc courses 
STAT5101 
Foundations of Data Science 
STAT5102 
Regression in Practice 
STAT5103 
Highdimensional Data Analysis 
STAT5104 
Data Mining 
STAT5105 
Applied Survival Data Analysis 
STAT5106 
Programming Techniques for Data Science 
STAT5107 
Discrete Data Analytics 
STAT6104 
Financial Time Series 
STAT6105 
Basic Actuarial Principles and Their Applications 
STAT6106 
Applied Bayesian Methods 
STAT6107 
Selected Topics on Data Science and Business Statistics 
STAT6108 
Official Statistics and Structural Equation Modelling 

RMSC5001 
Advanced Statistical Theory In Risk Management 
RMSC5002 
Principles of Risk Management 
RMSC5003 
Risk Measures 
RMSC5004 
Cases for Risk Management in Practice 
RMSC5101 
Statistical Methods in Risk Management and Finance 
RMSC5102 
Simulation Techniques in Risk Management and Finance 
RMSC6001 
Interest Rate and Fixed Incomes Risk Management 
RMSC6002 
Credit Risk Management 
RMSC6003 
Operational Risk Management 
RMSC6004 
Special Topics in Risk Management 
RMSC6005 
Special Topics in Quantitative Finance 
RMSC6006 
Portfolio theory with Risk Management Perspective 
*Note: If any discrepancy arises, the version on CUSIS should be treated as the official version.
Course Descriptions
STAT5005 Advanced Probability Theory
Measure theory concepts needed for probability. Expectation, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. (For MPhil & PhD students in Statistics)
STAT5010 Advanced Statistical Inference
This course is concerned with the fundamental theory of statistical inference. Topics include exponential families of distributions, sufficient statistics, convex loss functions, UMVU estimators, performance of the estimators, the information inequality and the principle of equivarlance. Bayes estimation, minimax estimation, largesample comparisons of estimators and asymptotic efficiency. (For MPhil & PhD students in Statistics and MPhil students in Risk Management Science)
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) fullrank linear models: least squares estimation, maximum likelihood estimation, simultaneous confidence intervals, tests of linear hypotheses, generalized least squares; 3) nonfullrank 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 NewtonRaphson, 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, nonnormal continuous data, hierarchical/heterogeneous data, longitudinal data, and incomplete data, will be discussed. (For MPhil & PhD students in Statistics)
STAT5101 Foundations of Data Science
The 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 indepth material presented in other courses of the programme. Topics include descriptive and graphical statistics, random variable, distribution, sampling distribution, estimation and hypothesis testing. The effective use of desktop productivity tools, such as spreadsheets, in the workplace environment for collecting and analyzing corporate data 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 Highdimensional Data Analysis
This course emphasizes statistical methods for analysing and interpreting highdimensional data that are common in business management, marketing research and other behavioral sciences. Selected topics include canonical correlations, classification, principal component, factor analysis, latent structure analysis and discrete multivariate methods. (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 decisionmaking, 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 timetoevent (survival or failuretime) 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 timetoevent data. The primary focus of the course is on the statistical methods designed for extraction of information from timetoevent data. This course introduces statistical theory and methodology for the analysis of timetoevent 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 preprocessing 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 twoway contingency tables, logistic regression, loglinear model, generalized linear model, classification and regression tree method.
STAT6040 Studies on Selected Topics II
Recent topics on computerintensive 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 nonstationary 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 (quasirandom 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, ValueatRisks, GARCH model in estimating volatility, time series methods in termstructure analysis, and data mining methods such as logistic regression, kmean 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 riskneutral pricing. It then proceeds to discuss the stochastic calculus foundations for continuoustime 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 BlackScholes formula for foreign exchange and termstructure 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 riskneutral valuation, arbitrage strategies, hedging strategies, implied volatilities and the Greeks. The ValueatRisk 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)
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, operational risk, integrated risk management and risk management Information Technology. (For MSc students in Risk Management Science)
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 ValueatRisk (VaR) such as historical simulation, parametric VaR, deltagamma approximation and MonteCarlo 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; 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; 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; 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 (quasirandom numbers), pricing American options and scenario simulation for risk management. (For MSc students in Risk Management Science)
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 HeathJarrowMorton (HJM) model as well as some termstructure models including HoLee, HullWhite and the CIR models. (For MSc students in Risk Management Science; 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 counterparties. This course aims at providing students with some quantitative methods in credit risk management. Ideas of reducedform 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)
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)
RMSC6004 Special Topics in Risk Management
The course aims at discussing recent advances in risk management. (For MSc students in Risk Management Science; 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; 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. Nonarbitrage 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; For MPhil students in Risk Management Science)
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)