FINANCIAL ECONOMETRICS - 2023/4
Module code: ECOM031
Introduction to modern econometric techniques used in the analysis of financial time series. Topics include ARIMA models, ARCH & GARCH models, estimating and testing the CAPM, fractional integration and nonlinear models (Markov-switching).
RISPOLI Luciano (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: L140
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 11
Independent Learning Hours: 95
Lecture Hours: 11
Guided Learning: 22
Captured Content: 11
Prerequisites / Co-requisites
Indicative content includes:
- Asset return processes: stationarity, random walks, tests for unit roots
- Conditional volatility: ARCH, GARCH, GARCH-M and E-GARCH models
- Estimating and testing the Capital Asset Pricing Model
- Long term memory and fractional integration in stock market returns
- Non-linear models including SETAR, STAR, Markov models of regime switching
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate
Knowledge of different econometric techniques used to model financial time series data series
The practical ability to build and assess appropriate econometric models using actual time series data
Ability to conduct independent research in the field of financial time series analysis, using rigorous statistical techniques
Ability in applying the knowledge acquired to real world financial and economic phenomena
Thus, the summative assessment for this module consists of:
A first individual assignment (worth 30% of the overall final mark) - connected with learning outcomes 1,2,4. A final individual assignment (worth 70% of the overall final mark) - connected with learning outcomes 1,3,4.
Both assessments give the chance to students to apply and showcase understanding of the theory studied in the lectures and understanding of the working of the software. These assessments typically involve conducting independent research on a selection of topics as part of the curriculum. The overall process typically includes gathering, analyzing and writing up the conclusions of the research project.
Formative assessment and feedback
Workshops to provide verbal feedback on the exercises which involve the use of software to build models of financial data series and help prepare students for the coursework. Office hours offer further feedback opportunities for students.
- To examine a variety of econometric techniques developed to analyse financial data series and use appropriate software to apply these techniques to estimate models of actual financial time series data. This module also equips students with the necessary analytical, statistical and software skills required to successfully undertake the writing of the MSc dissertation as part of the module ECOM027 Research Methods.
|001||Understand and critically evaluate models of asset return processes||KC||EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|002||Create models of ARCH and GARCH processes using appropriate software||KCPT||DIGITAL SKILLS; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|003||Analyse and create dynamic models with changing regimes using appropriate software||KCPT||DIGITAL SKILLS; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
|004||Estimate and test models of financial data using appropriate computer software||KPT||DIGITAL SKILLS; EMPLOYABILITY; RESOURCEFULNESS AND RESILIENCE|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The lectures provide an understanding of a variety of econometric techniques used in modelling the characteristic features of financial data series. The workshops give experience in applying appropriate software to estimate and critically assess different models of financial data series. Students benefit from the “hybrid system” (lectures + workshops) due to the implicit “learning by doing” approach which enforces an important level of applicability and relevancy of the topics studied.
The learning and teaching methods include:
- Lectures aimed at introducing the theoretical background of the studied topics; these are recorded and disseminated to students as captured content
- Workshops aimed at applying the theory to real world scenarios
- Guided learning aimed at deepening the understanding of the subject and complementing the material seen in class
- Students are expected to actively engage both in the lectures and seminars. In particular, students are expected to bring their laptop and actively solve the econometric exercises provided in the workshops.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: ECOM031
In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Students work in econometric software , collecting data and building econometric models
Resourcefulness & Resilience
Assessments feature real-world problem-based tasks and scenarios whilst also promoting students self-assessment of acquired knowledge in the subject.
Case studies and applications of econometric techniques as part of the toolkit of a professional economics / finance practitioner
Programmes this module appears in
|Economics MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Economics (Macroeconomics and Financial Markets) MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Economics and Finance MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Economics (Econometrics and Big Data) MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.