Durham University
Programme and Module Handbook

Postgraduate Programme and Module Handbook 2023-2024 (archived)

Module FINN43230: Financial Data Analysis and Econometrics Methods (Fudan DBA)

Department: Finance

FINN43230: Financial Data Analysis and Econometrics Methods (Fudan DBA)

Type Tied Level 4 Credits 30 Availability Available in 2023/24 Module Cap None.
Tied to N1R760


  • None


  • None

Excluded Combination of Modules

  • None


  • provide students with the necessary training to undertake advanced level research in the area of Finance and Economics;
  • provide students with an advanced understanding of the relevance and importance of alternative epistemological positions in the social sciences and the nature of both qualitative and quantitative approaches to research;
  • provide students with opportunities to be familiar with the frontier empirical and theoretical research in finance;
  • build upon students' knowledge of econometric methods and provide them with the specific advanced technical skills necessary to pursue empirical research in finance;
  • provide students with the tools required to model, analyse and predict financial markets.


  • Part I The research process 1. How to write a research paper 2. Using statistical software for financial data analysis 2.1 Data management 2.2 Estimation issues 3. Making use of and managing library facilities, databases and other learning resources
  • Examples of how to conduct research in different fields 1. Capital Structures 2. Collective behavior in capital markets 3. Foreign reserve management 4. Mergers and acquisitions
  • Part II 1. The statistical properties of univariate time series models and their application in Finance 2. Models of nonstationary time series 3. Cointegration and error-correction model 4. Cointegration in multivariate system 5. Modelling volatility 6. Further topics on ARCH 7. Forecasting in financial econometrics

Learning Outcomes

Subject-specific Knowledge:
  • By the end of this module, students should:
  • be aware of the significance of alternative epistemological positions when designing and undertaking research
  • be aware of, and familiar with, the facilities available for conducting literature searches and obtaining relevant data to facilitate empirical investigation
  • have an advanced knowledge of the principles and methods of modern financial econometrics
  • have extended and deepened their understanding of econometric methods, their application, and the interpretation of results at an advanced level
  • have improved their critical judgement and discrimination in the choice of techniques applicable to complex situations
Subject-specific Skills:
  • By the end of this module, students should:
  • be able to apply the core mathematical and statistical skills that underpin econometric analysis
  • be able to effectively organise, structure and manage a research project at an advanced level, including undertaking critical appraisal of relevant literature, and apply critical judgement and discrimination
  • have further developed the skills of inquiry, quantitative and qualitative research design, experimental research, data collection and information retrieval, bibliographic search, measurement and analysis, interpretation and presentation, self-discipline and time-management and the ability to work autonomously
  • have further practised problem solving skills at an advanced level and the use of econometric software
Key Skills:
  • Ability to make an initial formulation and articulation of a potential scheme of research
  • Ability to understand and resolve the problems and issues in undertaking doctoral research
  • Computer literacy
  • Transferring academic knowledge into practice

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • The module will be delivered in a workshop format over two intensive two-day teaching blocks. Workshops will comprise a balanced mix of lecture- and seminar-type delivery combined with small group discussions and other activities as appropriate to the nature of the material.
  • Learning will also occur through tutor-supported, as well as self-supported learning groups. In addition, guided reading will address key topics. This range of methods will ensure that students will acquire the advanced skills and knowledge to enable them to develop a thorough understanding of this specialist field of study.
  • The summative assessment is a 5000-word written assignment, designed to prepare students for subsequent stages of the programme ultimately the doctoral thesis/viva.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Workshop 4 Daily 8 32
Tutor-supported Learning Groups via webinars and other e-learning tools. With follow-up support as necessary using videoconferencing software. As required 48
Self-supported Learning group (self-organised by students, monitored by Fudan Office) 20
Preparation & Reading (reading list provided consisting of current published articles relevant to module content, available within library) 200
Total 300

Summative Assessment

Component: Written Assignment Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Individual written assignment that develops the initial formulation and articulation of a potential scheme of research 5,000 words max 100% same

Formative Assessment:

Individual oral examination, designed to test students' knowledge and understanding of the subject matter and their ability to articulate a researchable issue.

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University