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Accounting & Finance Finance and Compliance

Certificate for Module (Introduction to Financial Time Series Analysis)
證書(單元 : 金融時間序列分析入門)

CEF Reimbursable Course

CEF Reimbursable Course

Course Code
Application Code

Study mode
Start Date
26 Oct 2024 (Sat)
5 weeks
Course Fee
HK$7600 per programme
Deadline on 14 Oct 2024 (Mon)
2520 4612
2861 0278
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This programme helps you to develop skills in performing quantitative investment and financial analytics. Accept new application for October 2024 intake!


The programme aims to provide students with elementary knowledge of time series analysis for financial data. It introduces basic time series models, models and tests for long-run relationships, volatility models and simultaneous equation models. Computational tools will be used to analyze time series and build financial models. This programme is suitable for students to prepare for postgraduate studies in finance and investment disciplines.

Programme Details

On completion of the programme, students should be able to

  1. describe the characteristics of financial time series data and linear and non-linear time series models;
  2. explain long-run relationships models, volatility models and simultaneous equation models;  
  3. apply computational tools to investigate and analyze financial time series data; and
  4. discuss the applications of time series for financial modeling.


Application Code 2250-FN062A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 10:00am - 5:00pm
  • 30 hours
    5 meeting(s)
    6 hours per meeting


The programme consists of 30 contact hours with lectures and practical classes in computer laboratory.


(1) Fundamentals of Time-Series Models

  • Introduction to time series and financial econometrics using computational tools
  • The nature of financial time series data and times series decomposition
  • Brief review on regression analysis
  • Overview of linear time-series models
    • Stationary Time Series Models: moving average (MA) processes and MA models, autoregressive (AR) processes and AR models
    • Nonstationary Time Series Models: Autoregressive integrated moving average (ARIMA) models
    • Seasonal models
  • Overview of non-linear time series models
    • Exponential Smoothing: Simple Exponential Smoothing, Holt-Winters’ Seasonal Smoothing
    • Threshold Autoregressive (TAR) Models
    • Self-Exciting Threshold Autoregression (SETAR) Models


(2) Basic Long-Run Relationships Models

  • Stationarity and unit root testing: Dicky-Fuller test, Augmented Dickey-Fuller test, Philips-Perron test, KPSS test
  • Spurious regressions and cointegration
  • Engle-Granger test
  • Error-correction models
  • General to Specific Modelling


(3) Principles of Volatility Modeling

  • Heteroscedasticity
  • ARCH models
  • GARCH models
  • Extensions to GARCH models
  • Usage of GARCH type models
  • Forecasting variances using GARCH type models
  • Usage of variance forecasts


(4) Introduction to Simultaneous Equation Models (SEM)

  • Overview of Structural Model and Reduced-Form Model
  • The Identification of Simultaneous Equation Models
  • Parameter Estimation


(5) Basic Time Series Applications

  • Intervention Analysis and Outlier Detection
  • Longitudinal Analysis
  • Time Series Regression and Vector Time Series Models: VAR/ARCH Models
  • Spectral Analysis
  • Financial time series analysis and model building with computational tools

Assessment: class exercise (60%) & group presentation (40%)

Upon successful completion of the programme, students who have passed the continuous assessment and final assessment with attendance no less than 70% will be awarded within the HKU system through HKU SPACE the Certificate for Module (Introduction to Financial Time Series Analysis).


Class Details

  • Tentative timetable is subject to change and the course commencement is subject to sufficient enrollment
  • In case of cancel class, course fee will be refunded or transferred to next available intake

October 2024  Intake

Lecture Date Time
1 26 Oct 24 (Sat) 10:00-13:00 & 14:00-17:00
2  2 Nov 24 (Sat) 10:00-13:00 & 14:00-17:00
3  9 Nov 24 (Sat) 10:00-13:00 & 14:00-17:00
4 16 Nov 24 (Sat) 10:00-13:00 & 14:00-17:00
5 23 Nov 24 (Sat) 10:00-13:00 & 14:00-17:00

*(Class schedule will be given out upon one week prior to course commencement through by Email)


Course Fee
  • Course Fee : HK$7600 per programme

Entry Requirements

Applicants should hold an Advanced Diploma, a Higher Diploma or an Associate Degree preferably in the areas of business and statistics (e.g., mathematics, statistics, computer science, IT, engineering, economics or finance) awarded by a recognized institution. Applicants with other equivalent qualifications will be considered on individual merit.



(1) Mr. Alan Cheung

Mr. Alan Cheung, PRM, CQF, has solid skills in the applications of the finance time series analysis and financial model building. Also, he has solid experience in fintech in top tier investment banks, versed in architecting low latency, high frequency algorithmic trading systems. He is currently a Quantitative Strategist on the Equities Desk in Bank of America Merrill Lynch. Alan has a Master of Science in Mathematical and Computational Finance from the University of Oxford after graduating with First Class Honours in Mathematics with Statistics for Finance from Imperial College London. Currently, Mr. Alan Cheung is teaching modules under Postgraduate Diploma in Applied Financial Engineering and Postgraduate Diploma in Finance and Data Analytics.


(2) Dr. Zenki Kwan

Dr. Zenki Kwan, FRM, CAIA, CB, is good at quantitative finance and financial time series analysis. He is the investment director of a listed company and a family office in Hong Kong, responsible for investment strategy and portfolio management across equities, fixed income, currency, funds and structured products. He has previously worked in J.P. Morgan, UBS, McKinsey and Samsung Securities. In addition to his doctoral degree, Dr. Kwan also holds Master of Finance and Master of Applied Business Research degrees and completed executive education programs at Harvard Law School and Oxford University Saïd Business School, respectively. Dr. Kwan is currently teaching modules under Postgraduate Diploma in Applied Financial Engineering and Postgraduate Diploma in Finance and Data Analytics.


(3)  Mr. Ferrix Lau

Mr. Ferrix Lau, ACS, ACIS, CFA, FRM  has over 10 years’ teaching experience in business, accounting and finance modules at tertiary level. He teaches Financial Analysis, Financial Risk Management, Quantitative Analysis, Financial Accounting, Cost and Management Accounting as well as Corporate Governance. Moreover, he is a co-author of a Statistics book, Quantitative Analysis for Professional Studies and Projects. Furthermore, he has strong interests in the areas of Statistical Analysis, Quantitative Finance and Machine Intelligence. Mr. Lau has earned a Bachelor's Degree in Social Science from The Chinese University of Hong Kong, major in Economics and minor in Computer Science. Besides, he holds a Master's Degree in Business Administration with Distinction from The University of Hong Kong, concentrating on the theme of Accounting Control and Financial Management.


(4) Mr. Kenrick Yeung

Mr. Kenrick Yeung received one MSc in Data Science and Business Statistics from The Chinese University of Hong Kong, and another MSc in Applied Economics from Hong Kong Baptist University, and his bachelor degree in Economics and Finance in Hong Kong Shue Yan University. He has been teaching Microeconomics, Macroeconomics, Statistics, Econometrics, Engineering Economics, Geopolitics and Corporate Finance.


(5) Mr. Felix Chan

Mr. Felix Chan, FRM, ACAMS holds a Master's Degree in Data Science from The University of Hong Kong, and his bachelor degree in Quantitative Finance and Risk Management Science in the Chinese University of Hong Kong. He has strong interests in the areas of Big Data, Machine Learning and Statistical Analysis. Besides, he has years of experience working  at multiple major technology companies, through which he has help corporates in various sectors including finance, government, professional services, etc. realize value through technology and data science.




  • The CEF Institution Code of HKU SPACE is 100
CEF Courses
Certificate for Module (Introduction to Financial Time Series Analysis)
證書(單元 : 金融時間序列分析入門)
COURSE CODE 33C13554A FEES $7,600 ENQUIRY 2520-4612
Continuing Education Fund Continuing Education Fund
This course has been included in the list of reimbursable courses under the Continuing Education Fund.

Certificate for Module (Introduction to Financial Time Series Analysis)

  • This course is recognised under the Qualifications Framework (QF Level [5])


Online Application Apply Now

Application Form Download Application Form

Enrolment Method
Payment Method
1. Cash, EPS, WeChat Pay Or Alipay

Course fees can be paid by cash, EPS, WeChat Pay or Alipay at any HKU SPACE Enrolment Centres.

2. Cheque Or Bank draft

Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify the programme title(s) for application and the applicant’s name.. You may either:

  • bring the completed form(s), together with the appropriate course or application fees in the form of a cheque, and any required supporting documents to any of the HKU SPACE enrolment centres;
  • or mail the above documents to any of the HKU SPACE Enrolment Centres, specifying  “Course Application” on the envelope.  HKU SPACE will not be responsible for any loss of payment sent by mail.
3. VISA/Mastercard

Applicants may also pay the course fee by VISA or Mastercard, including the “HKU SPACE Mastercard”, at any HKU SPACE enrolment centres. Holders of the HKU SPACE Mastercard can enjoy a 10-month interest-free instalment period for courses with a tuition fee worth a minimum of HK$2,000; however, the course applicant must also be the cardholder himself/herself. For enquiries, please contact our staff at any enrolment centres.

4. Online Payment

Online application / enrolment is offered for most open admission courses (course enrolled on first come, first served basis) and selected award-bearing programmes. Application fees and course fees of these programmes/courses can be settled by using "PPS by Internet" (not available via mobile phones), VISA or Mastercard. In addition to the aforesaid online payment channels, continuing students of award-bearing programmes, if their programmes offer online service, may also pay their course fees by Online WeChat Pay, Online Alipay and Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.


  • If the programme/course is starting within five working days, application by post is not recommended to avoid any delays. Applicants are advised to enrol in person at HKU SPACE Enrolement Centres and avoid making cheque payment under this circustance.
  • Fees paid are not refundable except under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment), subject to the School’s discretion. In exceptional cases where a refund is approved, fees paid by cash, EPS, WeChat Pay, Alipay, cheque or PPS (for online payment only) will normally be reimbursed by a cheque, and fees paid by credit card will normally be reimbursed to the payment cardholder's credit card account.
  • In addition to the published fees, there may be additional costs associated with individual programmes. Please refer to the relevant course brochures or direct any enquiries to the relevant programme team for details.
  • Fees and places on courses cannot be transferrable from one applicant to another. Once accepted onto a course, the student may not change to another course without approval from HKU SPACE. A processing fee of HK$120 will be levied on each approved transfer.
  • Receipts will be issued for fees paid but HKU SPACE will not be repsonsible for any loss of receipt sent by mail.
  • For payment certification, please submit a completed form, a sufficiently stamped and self-addressed envelope, and a crossed cheque for HK$30 per copy made payable to "HKU SPACE" to any of our enrolment centres.