Module ECOS3221: FINANCIAL ECONOMETRICS

Department: Business School (Economics and Finance) [Queen's Campus, Stockton]

ECOS3221: FINANCIAL ECONOMETRICS

Prerequisites

• Introduction to Financial Econometrics.

Corequisites

• None.

Excluded Combination of Modules

• None.

Aims

• This module aims to provide students with a rigorous grounding in financial econometrics.
• encourage students to critically appraise work in this area and to facilitate students' analytical skills.

Content

• Content of this module will investigate contemporaneous financial econometric tools.
• The following is an indication of the material of the material covered:
• Review of the Linear Regression Model
• Univariate Stationarity Time Series Processes: Autoregressive model, moving average model, ARMA model, exponential smoothing
• Non-Stationarity and Unit Root Tests: Stationary / nonstationary time series, spurious regression, unit root process, unit root tests
• The Engle and Granger Cointegration Procedure: Concept of cointegration, error-correction model, testing for cointegration (Engle-Granger approach)
• Introduction to Johansen Cointegration test: Testing for cointegration (Johansen approach)
• Modelling volatility: Autoregressive conditional heteroscedastic (ARCH) model, motivation of ARCH model, testing for ARCH, estimating the ARCH model
• Further topics on ARCH : Generalized autoregressive conditional heteroscedastic (GARCH) model, ARCH-in-mean model, asymmetric GARCH models, EGARCH models
• Forecasting in financial econometrics: An introduction to forecasting, forecasts with univariate time series model and GARCH model, methods for forecast evaluation

Learning Outcomes

• Have become familiar with econometric tools employed in Finance
• Be able apply and interpret econometric techniques

• Be able to implement and interpret statistical tests to discriminate between stationary and non-stationary time series and to be able to model the series appropriately
• Be able to implement and interpret statistical tests to determine the presence of contegration between pairs of non-stationary time series and be able to model the series appropriately
• Be able to appropriately model time series which display significant time variation in conditional variance.

• Written communication, via summative assessment.
• Planning and Organising - e.g. by observing the assignment deadlines
• Problem solving, via understanding the technical problems assessed by summative work, as well as the analytical and quantitative skills of econometrics.
• Initiative, in searching the relevant literature, for the summative assignment
• Numeracy, required for understanding and applying the mathematical and statistical tools that underpin econometric analysis.
• IT skills, via usage of econometric software for the statistical analysis of data, and word-processing, required for the presentation of the summative assignment.

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

• Teaching is by lectures, seminars and computer labs. Learning takes place through attendance at lectures, preparation for and participation in seminar classes, and private study. Formative assessment is by means of an essay. Summative assessment is by means of a written assignment.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

1500 word essay.

■ 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