Module Directory

The aim of this module is to provide first year PhD students, registered for a PhD in either Finance or Econometrics in Essex Business School, with an up-to-date and rigorous introduction to a range of state-of-the art methods used in the published financial econometrics literature.

This module aims to introduce students to the core methodology and empirical practice of modern financial econometrics. This will involve developing relevant statistical and mathematical skills, together with the ability to apply these methods to real data using industry-standard software and to interpret their results

The aims of this module are:

• To develop and transmit knowledge of recent advancements in the financial econometrics literature through examining key papers that are closely tied to the research interests of our academic staff.


• To provide training in relevant econometric techniques required to perform robust research in financial econometrics, and to illustrate the use of these techniques in practice.


• To provide students with hands on experience of implementing the methods commonly employed in the financial econometrics literature through a series of lectures and associated workshops and computer practical sessions. 


• To provide training in the use of the relevant computing software. 

By the end of this module, students will be expected to be able to:

1. Have an understanding of the relevant econometric techniques appropriate for performing both theoretical and empirical analysis in financial econometrics.


2. Have developed knowledge on the use of industry standard statistical software, being able to use this software to perform advanced statistical techniques on either user-imported or pseudo-data.


3. Be able to critically evaluate contributions made by recent papers in the financial econometrics literature, and to replicate or develop further the findings outlined in these papers

This module will be run over the autumn term. The module will consist of 10 weeks of lecture plus workshop (group based) and/or computer lab based teaching around a number of specific advanced topics in financial econometrics. Each topic will be delivered by a particular member of EBS staff who undertakes research in that area (broadly defined).

The selection of topics covered will vary from year-to-year depending on staff availability and will also be designed to reflect the interests and chosen areas of doctoral research of that year's cohort of students. Likely topics include: Advanced unit root and cointegration methods; Detecting rational bubbles in financial price data; Predictive regression methods for forecasting stock returns; Econometrics of asset pricing models; Machine learning and `big data' methods in finance; Cross sectional and panel data methods in finance.

Students will be asked to read key published journal articles (which will vary from year to year, all of which will be accessible on-line using their university ID and password) relevant to each topic which will be central to the teaching delivery. Associated lecture notes will be provided for each topic covered.

This module will be delivered via:

• One 2-hour lecture per week
• One 2-hour lab per week