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Accounting & Finance FinTech and Financial Analytics

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Certificate for Module (Financial Econometrics and Risk Modelling)
證書(單元 : 金融計量經濟學與風險建模)

Course Code
FN158A
Application Code
2365-FN158A

Credit
6
Study mode
Part-time
Start Date
10 Jan 2026 (Sat)
Next intake(s)
Mar 2026
Duration
30 hours
Language
English
Course Fee
Course Fee: HK$10,500 per programme (* course fees are subject to change without prior notice)
Deadline on 29 Dec 2025 (Mon)
Enquiries
2867 8331 / 2867 8424
2861 0278
Apply Now

Today and Upcoming Events

10
Dec 2025
(Wed)

How to Design a Strong-Stock Analytics Dashboard System (强勢股分析系統)? (10 Dec 2025)

To design a stock price analytics system, we need to do the following: Collect historical stock prices Transform the collected stock price record to an appropriate format for presentation Present the transformed stock price datasets in a useful layout to facilitate analytics and investors’ review.   In this talk (webinar), the speaker will showcase how to design a Strong Stock Analytics Dashboard with a BI approach. This would give you a fresh view of the practical use of data automation and data visualization techniques.   During this webinar, you will explore how a Strong Stock Analytics Dashboard will help you to: review the recent trend of HSI identify the strong stocks and weak stocks based on a specified definition of price momentum compare the ratio between the strong stocks and weak stocks according to your selected price momentum definition do sectoral analysis of the strong / weak stocks   This is an advanced application of data analytics techniques with common financial data.  You will find this webinar inspiring and will give you food for thought on how to make use of learnt data techniques for financial stock investment analysis.   Sample Screenshots below:   Related Programme Links: Certificate for Module (Technical Analysis and Data Analytics for Stock Investment) - HKU SPACE: FinTech and Financial Intelligence, Data Science courses https://hkuspace.hku.hk/prog/exe-cert-in-interpretation-and-visualization-of-business-big-data https://hkuspace.hku.hk/prog/cert-for-module-business-intelligence-and-data-automation

17
Dec 2025
(Wed)

Any correlation between Bitcoin, Ethereum and Gold? And now Stablecoin Explained (17 Dec 2025)

Bitcoin and Ethereum are the most dominant cryptocurrencies, both accumulative account for over 90% in term of market capitalization excluding stablecoin, out of more than two thousands currently in the market. They are both in a form of digital asset that trades via various DEX or centralizated regulated trading platforms. However there are quite many key differences among them though. Bitcoin (BTC) is designed as a monetary storage (some proclaim as alternative of Gold) and medium of transaction and as an alternative to fiat currency. Ethereum (ETH), otherwise, is intended for complex smart contracts or dApps which contribute and act as key infrastructure of the emerging Web3.0 future. In light of recent rapid further innovation (like staking protocol) and adoption, the price of BTC and ETH have been risen more than double in past 12 months and also exhibited a huge volatility. The speaker will give a brief introduction of above crypto with some attention drawn to the relationship and correlation among Bitcoin, Ethereum, Gold, XRP and S&P500 – as shown in below charts. The speaker will then also talk about the latest development and impacts of the recently passed GENIUS Act in U.S. and the Stablecoins Ordinance (Cap. 656) in HK. To say, Stablecoin per se. is NOT referring the price to be fixed or stable, it’s referring to link or collateralize by some tangible asse shifting from no intrinsic value issue. At 10 Oct 2025, one of most selloff in crypto, USDe has experienced flash crash to as low as 0.65 USDEUST, per shown below. Source: Bloomberg   Language: Cantonese (Supplemented with English)

Accept New Applications for Jan 2026 intake! There will be practical classes in the computer laboratory. Contemporary knowledge in financial econometrics and risk modelling will be introduced. Applications of computational tools and software for quantitative risk analysis and building risk models will be illustrated. Practical applications of financial econometrics, financial risk measurement and management will be discussed.

Highlights

The programme is designed to provide students with contemporary knowledge in financial econometrics and risk modelling. It equips them with practical skills to apply computational tools and software for quantitative risk analysis and building risk models. Besides, the programme covers practical applications of financial econometrics, financial risk measurement and management.

Programme Details

On completion of the programme, students should be able to

  1. examine the theories of financial econometrics and time series data;
  2. assess financial risks and apply quantitative techniques for integrated risk management;
  3. apply computational tools and software to analyse financial data; and
  4. develop risk models and discuss issues of risk management.
Application Code 2365-FN158A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 10:00am - 6:30pm
Duration
  • 30 hours per programme
Venue
  • Hong Kong Island Learning Centre
  • Kowloon West Campus
  • Kowloon East Campus

Modules

Course Content :

(1) Introduction to financial econometrics

  • Overview of financial econometrics
  • Introduction to computational tools and software for financial econometrics
  • Time series data and financial analytics
  • Stationarity and unit roots testing
  • Autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models
  • Autoregressive conditional heteroskedasticity (ARCH) and generalised autoregressive conditional heteroskedasticity (GARCH) models
  • Cointegration and error correction models: vector autoregression (VAR) and vector error correction model (VECM)
  • High-frequency financial data analysis
  • Limited dependent variables
  • Panel data analysis

(2) Techniques of quantitative risk analysis and management

  • Introduction to risk management and risk measurement
  • Machine learning (ML) for financial risk analysis and management
  • Scenario analysis
  • Stress testing
  • Volatility modelling
  • Correlation modelling
  • Monte Carlo simulation
  • Portfolio optimisation
  • Back-testing

(3) Applications of financial econometrics and risk modelling

  • Risk measurement and financial econometrics
  • Market risk modelling: value at risk (VaR) and conditional value at risk (CvaR)
  • Credit risk modelling: probability of default (PD), loss given default (LGD) and exposure at default (EAD)
  • Liquidity risk modelling: liquidity gap analysis and cash flow projections
  • Operational risk modelling and extreme value theory (EVT) in risk management
  • Risk management with applications of artificial intelligence (AI) and financial econometrics
  • Quantitative risk analysis and integrated risk management
  • Issues of financial risk management and investment decision-making

Assessment method: In-class Exercise + Group Project Presentation

 

Award

Upon successful completion of the programme, students who have passed the assessments with attendance no less than 70% will be awarded within the HKU system through HKU SPACE a "Certificate for Module (Financial Econometrics and Risk Modelling)".

Class Details

Timetable

Lecture Date Time
1 10 Jan 26 (Sat) 10:00-13:00 & 14:00-18:30
2 17 Jan 26 (Sat) 10:00-13:00 & 14:00-18:30
3 24 Jan 26 (Sat) 10:00-13:00 & 14:00-18:30
4 31 Jan 26 (Sat) 10:00-13:00 & 14:00-18:30

Remarks: Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers 

Progression Path

Teacher Information

Dr Francis Lau

Background

Dr Lau is a seasoned financial data analytics practitioner, and a professional trainer in finance-related disciplines. He has over 22 years of experience in business planning, data analytics, management information, regulatory compliance, and risk management acquired from working for multinational analytics vendors, banks, consulting firms, and universities. Dr Lau is a subject matter expert in applying data analytics to enhance business decision-making, constructing quantitative models to gauge business performance, and streamlining the management reporting processes. In addition to industry experiences, Dr Lau also has extensive exposures in developing and delivering academic and professional education programs for financial institutions, professional associations and universities. Dr Lau is a well-recognized trainer in compliance, data science, financial markets, risk management, and sustainability.

Mr Ken Choi

Background

Mr Choi has over ten years of practical working experience across start-ups, data software companies, retail banking, insurance companies, listed companies, and large multinational corporation firms in areas of AI, business intelligence, big data and machine learning. He works as a senior project data analyst in charge of business process automation and data projects powered by AI, Machine Learning and Deep Learning. Moreover, he would like to leverage the Web 3.0 technology interaction with FinTech. Furthermore,  he applies business analytics to predict the revenue growth of the corporation. He has been actively teaching and coaching in different institutes and schools over the decade. He is a professional in communicating abstract data concepts and theory to non-tech students efficiently and effectively. He holds a Bachelor (Hons) in Statistics and Operation Research from HKBU and a Master of Statistics and Risk Management from HKU. He is a certified FRM (Financial Risk Manager), Certified Statistical Business Analyst, Certified Predictive Modeler: Enterprise Miner, Certified Advanced Programmer, Certified Base Programmer, and Certified System Platform Administrator. All 5 Credentials were approved by SAS Institute.

Fee

Application Fee

HK$150

Course Fee
  • Course Fee: HK$10,500 per programme (* course fees are subject to change without prior notice)

Entry Requirements

Applicants should hold an Advanced Diploma, a Higher Diploma or an Associate Degree awarded by a recognised institution where the language of teaching and assessment is English. Those with a business, accounting, finance, economics, mathematics, statistics, science, engineering, IT or computer science background would have an advantage.

Applicants with other equivalent qualifications will be considered on individual merit.

**Please upload copy of HKID and proof of degree while applying online

Apply

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 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 personal information and 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 (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, new and continuing students of award-bearing programmes with available online service, they may also pay their course fees by Online WeChat Pay, Online Alipay or Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.

Notes

  • 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 Enrolment Centres and avoid making cheque payment under this circumstance.

  • 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, FPS or PPS by Internet will be reimbursed by a cheque, and fees paid by credit card will be reimbursed to the credit card account used for payment. 

  • 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.
  • HKU SPACE will not be responsible for any loss of payment, receipt, or personal information 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.