| 授業名 | Financial Econometric Modelling |
|---|---|
| Course Title | Financial Econometric Modelling |
| 担当教員 Instructor Name | Xinyang Wei |
| コード Couse Code | NUC436_N24B |
| 授業形態 Class Type | 講義 Regular course |
| 授業形式 Class Format | On Campus |
| 単位 Credits | 2 |
| 言語 Language | EN |
| 科目区分 Course Category | 専門教育科目 / Specialized Subject |
| 学位 Degree | BBA |
| 開講情報 Terms / Location | 2024 UG Nisshin Term3 |
授業の概要 Course Overview
Misson Statementとの関係性 / Connection to our Mission Statement
Aligning with the NUCB Undergraduate School's mission to foster innovative leaders with a global perspective and problem-solving capabilities, this course is designed to cultivate participants' ability to think creatively and analytically. By engaging with advanced empirical techniques and applying them to real-world financial scenarios, participants will develop a strong foundation in data analysis, enhancing their capacity to tackle complex financial and economic challenges with an exploratory and solution-oriented mindset.
授業の目的（意義） / Importance of this course
Financial data, such as stock market returns, interest rates, exchange rates, and commodity or asset prices, often exhibit unique statistical characteristics that require specialized analytical techniques. This course aims to provide a deep dive into the empirical methods widely used in financial market analysis, focusing on their practical applications to real data. Key topics covered will include understanding the distinct features of financial data, such as non-normal distributions, volatility clustering, and leverage effects, as well as advanced concepts like time-varying volatility and risk modeling through ARCH and GARCH models. Additionally, the course will explore the dynamic relationships between different financial series using autoregressive (AR), moving average (MA), and combined ARMA models. By learning these techniques, participants will be equipped with valuable skills that are highly relevant to both public policy and financial industry roles. The course emphasizes hands-on experience by utilizing the R programming language for statistical and econometric analysis, enabling students to conduct empirical studies and data-driven decision-making.
到達目標 / Achievement Goal
By the end of this course, participants will have a thorough understanding of the fundamental and advanced quantitative methods required for analyzing financial and economic data. They will be able to critically evaluate financial models, construct and apply time series and volatility models, and effectively interpret their results in the context of real-world financial data problems. Participants will also develop the competency to independently conduct empirical research and data analysis using R, preparing them to contribute meaningfully to the fields of finance, economics, and beyond.
本授業の該当ラーニングゴール Learning Goals
*本学の教育ミッションを具現化する形で設定されています。
LG1 Critical Thinking
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
LG2 Diversity Awareness
LG3 Ethical Decision Making
LG4 Effective Communication
LG6 Managerial Perspectives (BBA)
受講後得られる具体的スキルや知識 Learning Outcomes
After completing the course, participants are expected to demonstrate the following skills and knowledge:
1 - Advanced Analytical Skills: Ability to critically analyze real financial data, such as stock prices, interest rates, exchange rates, and other asset prices, using advanced quantitative methods. Participants will learn to identify key patterns and characteristics, such as volatility clustering, leverage effects, and non-stationarity, and apply appropriate time series models (e.g., AR, MA, ARMA, ARCH, GARCH) to derive meaningful insights.
2 - Quantitative Analysis Framework Development: Proficiency in establishing robust quantitative analysis frameworks tailored to different types of financial data problems. Participants will gain hands-on experience using R programming for econometric modeling, data visualization, and statistical inference, equipping them with the skills needed to handle complex data sets and perform rigorous empirical research.
3 - Modeling and Forecasting Proficiency: Competence in constructing and applying various time series models, including autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and volatility models such as ARCH and GARCH, for forecasting and risk assessment in financial markets. Participants will be able to assess model performance, refine models for better accuracy, and interpret results within the context of financial decision-making.
4 - Effective Communication and Presentation Skills: The ability to construct well-organized, logical, and professional written reports on financial data analysis. Participants will learn to clearly communicate complex quantitative findings and insights to both technical and non-technical audiences, using a concise and coherent narrative style that effectively supports data-driven decision-making.
5 - Practical Application in Real-World Contexts: A comprehensive understanding of how to apply quantitative techniques and econometric models to real-world financial problems in both public and private sectors. Participants will be equipped to address challenges such as financial market volatility, asset pricing, risk management, and policy evaluation, leveraging their analytical skills and knowledge to provide practical solutions.
These learning outcomes will prepare participants for careers in finance, economics, policy-making, and data science, where the ability to analyze, model, and communicate financial data is crucial.
1 - Advanced Analytical Skills: Ability to critically analyze real financial data, such as stock prices, interest rates, exchange rates, and other asset prices, using advanced quantitative methods. Participants will learn to identify key patterns and characteristics, such as volatility clustering, leverage effects, and non-stationarity, and apply appropriate time series models (e.g., AR, MA, ARMA, ARCH, GARCH) to derive meaningful insights.
2 - Quantitative Analysis Framework Development: Proficiency in establishing robust quantitative analysis frameworks tailored to different types of financial data problems. Participants will gain hands-on experience using R programming for econometric modeling, data visualization, and statistical inference, equipping them with the skills needed to handle complex data sets and perform rigorous empirical research.
3 - Modeling and Forecasting Proficiency: Competence in constructing and applying various time series models, including autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and volatility models such as ARCH and GARCH, for forecasting and risk assessment in financial markets. Participants will be able to assess model performance, refine models for better accuracy, and interpret results within the context of financial decision-making.
4 - Effective Communication and Presentation Skills: The ability to construct well-organized, logical, and professional written reports on financial data analysis. Participants will learn to clearly communicate complex quantitative findings and insights to both technical and non-technical audiences, using a concise and coherent narrative style that effectively supports data-driven decision-making.
5 - Practical Application in Real-World Contexts: A comprehensive understanding of how to apply quantitative techniques and econometric models to real-world financial problems in both public and private sectors. Participants will be equipped to address challenges such as financial market volatility, asset pricing, risk management, and policy evaluation, leveraging their analytical skills and knowledge to provide practical solutions.
These learning outcomes will prepare participants for careers in finance, economics, policy-making, and data science, where the ability to analyze, model, and communicate financial data is crucial.
SDGsとの関連性 Relevance to Sustainable Development Goals
Goal 4 質の高い教育をみんなに（Quality Education）
教育手法 Teaching Method
| 教育手法 Teaching Method | % of Course Time | |
|---|---|---|
| インプット型 Traditional | 30 % | |
| 参加者中心型 Participant-Centered Learning | ケースメソッド Case Method | 70 % |
| フィールドメソッド Field Method | 0 % | 合計 Total | 100 % |
事前学修と事後学修の内容、レポート、課題に対するフィードバック方法 Pre- and Post-Course Learning, Report, Feedback methods
Course Prerequisites
1 - Before taking this course, participants are highly recommended to have a basic understanding of statistical and linear regression topics, such as mean and variance, correlation coefficients, covariance, hypothesis testing, and regression analysis. These topics are covered in previous NUCB courses, including Introduction to BBA and Business Statistics. For those who have not yet studied these topics, relevant content can be found in the related chapters of the following book. We encourage participants to review this material in advance of the course start:
https://2012books.lardbucket.org/books/beginning-statistics/
2 - A laptop with R installed is required for financial data analysis and case studies throughout the course. Introductory support will be provided by the instructor for participants who have not used R before.
3 - It is recommended that participants spend at least 3 hours preparing for each case, including reviewing the fundamental knowledge provided in the casebook. Participants seeking deeper insights may read the relevant chapters of the textbook, but the course will primarily be based on the content in the casebook.
Class Discussion
1 - The course emphasizes interactive class discussions to reinforce key financial econometrics concepts. Participants will connect theoretical knowledge with practical applications, enhancing their ability to make data-driven financial decisions.
2 - Core topics like time series modeling, volatility estimation, and risk assessment will be examined through case studies and collaborative discussions. The instructor will guide participants through analytical processes, promoting critical thinking and practical problem-solving skills.
Feedback Methods
1 - The course will feature regular quizzes to assess participant progress, with feedback provided to improve learning outcomes and ensure mastery of concepts.
2 - Constructive feedback will be a cornerstone of the learning experience, offering chances for one-on-one consultations with the instructor. During these sessions, participants can discuss their individual progress, seek clarification on any doubts, and obtain personalized guidance.
1 - Before taking this course, participants are highly recommended to have a basic understanding of statistical and linear regression topics, such as mean and variance, correlation coefficients, covariance, hypothesis testing, and regression analysis. These topics are covered in previous NUCB courses, including Introduction to BBA and Business Statistics. For those who have not yet studied these topics, relevant content can be found in the related chapters of the following book. We encourage participants to review this material in advance of the course start:
https://2012books.lardbucket.org/books/beginning-statistics/
2 - A laptop with R installed is required for financial data analysis and case studies throughout the course. Introductory support will be provided by the instructor for participants who have not used R before.
3 - It is recommended that participants spend at least 3 hours preparing for each case, including reviewing the fundamental knowledge provided in the casebook. Participants seeking deeper insights may read the relevant chapters of the textbook, but the course will primarily be based on the content in the casebook.
Class Discussion
1 - The course emphasizes interactive class discussions to reinforce key financial econometrics concepts. Participants will connect theoretical knowledge with practical applications, enhancing their ability to make data-driven financial decisions.
2 - Core topics like time series modeling, volatility estimation, and risk assessment will be examined through case studies and collaborative discussions. The instructor will guide participants through analytical processes, promoting critical thinking and practical problem-solving skills.
Feedback Methods
1 - The course will feature regular quizzes to assess participant progress, with feedback provided to improve learning outcomes and ensure mastery of concepts.
2 - Constructive feedback will be a cornerstone of the learning experience, offering chances for one-on-one consultations with the instructor. During these sessions, participants can discuss their individual progress, seek clarification on any doubts, and obtain personalized guidance.
授業スケジュール Course Schedule
第1日（Day1）
Introduction to Financial Data Analysis Using R●使用するケース
Introduction to R: Data Import, Descriptive Statistics, and Plotting第2日（Day2）
Developing Autoregressive Models for Time Series Analysis●使用するケース
Constructing an Autoregressive Model: Understanding the Dynamics of UK House Prices第3日（Day3）
Utilizing Moving Average Models in Financial Data Analysis●使用するケース
Constructing a Moving Average Model: Analyzing UK House Price Dynamics第4日（Day4）
Integrating AR and MA Models for Comprehensive Time Series Modeling●使用するケース
Constructing an Autoregressive Moving Average Model: Modeling the Dynamics of UK House Prices第5日（Day5）
Applying ARMA Models for Forecasting in Financial Markets●使用するケース
Forecasting Using ARMA Models: Analyzing Japan's GDP Growth Rate第6日（Day6）
Modeling Financial Market Volatility Using Autoregressive Conditional Heteroskedasticity (ARCH) Models●使用するケース
Volatility Modeling with an ARCH Model: Analyzing Exchange Rate Returns第7日（Day7）
Advanced Techniques in Volatility Modeling with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Models●使用するケース
Volatility Modeling with a GARCH Model: Analyzing U.S. Stock Market ReturnsThis is a tentative list, and the teaching content and progress as well as the cases to be used may be adjusted according to the actual situation.
成績評価方法 Evaluation Criteria
*成績は下記該当項目を基に決定されます。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
*クラス貢献度合計はコールドコールと授業内での挙手発言の合算値です。
| 講師用内規準拠 Method of Assessment | Weights |
|---|---|
| コールドコール Cold Call | 0 % |
| 授業内での挙手発言 Class Contribution | 40 % |
| クラス貢献度合計 Class Contribution Total | 40 % |
| 予習レポート Preparation Report | 20 % |
| 小テスト Quizzes / Tests | 0 % |
| シミュレーション成績 Simulation | 0 % |
| ケース試験 Case Exam | 0 % |
| 最終レポート Final Report | 40 % |
| 期末試験 Final Exam | 0 % |
| 参加者による相互評価 Peer Assessment | 0 % |
| 合計 Total | 100 % |
評価の留意事項 Notes on Evaluation Criteria
教科書 Textbook
- Chris Brooks「Introductory Econometrics for Finance」Cambridge University Press（2019）
- Dimitrios Asteriou and Stephen G. Hall「Applied Econometrics」Bloomsbury Publishing（2021）
参考文献・資料 Additional Readings and Resource
The textbooks are intended for reference use. Previous versions of the textbooks are also acceptable for use in this course.
授業調査に対するコメント Comment on Course Evaluation
The course structure and content will be refined and updated based on feedback and recommendations from previous participants.
担当教員のプロフィール About the Instructor
Dr Xinyang Wei is an Associate Professor at NUCB with a PhD in Economics from the University of New South Wales, Sydney. His research explores intricate aspects of energy and environmental economics, with a focus on policy evaluation, climate change dynamics, and the pursuit of low-carbon development. Recognised for his exemplary research, he was granted the Herbert Smith Freehills Law and Economics Higher Degree Research Award. His scholarly contributions are reflected in publications across renowned academic journals, including Energy Economics, Energy, Renewable Energy, Renewable and Sustainable Energy Reviews, International Journal of Energy Research, and the Journal of Industrial Ecology.
（実務経験 Work experience）
Before joining NUCB, he accumulated enriching teaching and research experiences at both the University of New South Wales and the Macau University of Science and Technology. He possesses a profound background in supervising undergraduate, master's, and PhD theses, and has a versatile teaching portfolio spanning courses like Business Statistics, Data Analysis, Financial Data Analysis, Econometrics, Intermediate Econometrics, Financial Statistics and Econometrics, Financial Risk Management and Research Methodology. His dedication to excellence in education was recognised in Macau with the First Prize in the University Teaching Achievement Award.
Refereed Articles
- (2024) Variance dynamics and term structure of the natural gas market. Energy Economics
- (2024) Eco-Financial Dynamics: How Green Finance and Renewable Energy Are Shaping a New Economic Era. NUCB Business Review
- (2023) Study on the spatial spillover effect and path mechanism of green finance development on China's energy structure transformation. Journal of Cleaner Production
- (2023) Effect of green finance reform and innovation pilot zone on improving environmental pollution: an empirical evidence from Chinese cities. Environmental Science and Pollution Research
- (2023) The Impact of Fintech Development on Air Pollution. International Journal of Environmental Research and Public Health