Introductory description

The purpose of this module is to provide a general introduction to econometric techniques used for modeling and understanding financial markets. Upon finishing the module, students should have a solid grasp of several important models in financial econometrics.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module includes a selection of the following topics:

• Linear Regression
• Instrumental Variables
• Maximum Likelihood Estimation
• High-Frequency Data and Econometrics
• Cross Section of Expected Returns
• Observable or Unobservable Factor Model, and Factor Construction in Practice
• State-Space Models and Filtering
• Econometrics of Options
• Generalized Method of Moments Estimation
• Hedging Climate Risks
• Additional Advanced Topic

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of mathematical and statistical tools and techniques used in quantitative finance
• Think quantitatively and critically

Indicative reading list

• John Y. Campbell: Financial Decisions and Markets, Princeton University Press. 2017
• John Y. Campbell, Andrew W. Lo and A. Craig MacKinlay: The Econometrics of Financial Markets, Princeton University Press.
• Yacine Aït-Sahalia and Jean Jacod: High Frequency Financial Econometrics, Princeton University Press. 2014
• John H. Cochrane: Asset Pricing, Princeton University Press. 2005.
• James D. Hamilton: Time Series Analysis, Princeton University Press.
• Stephen J. Taylor: Asset Price Dynamics, Volatility and Prediction, Princeton University Press. 2007

Interdisciplinary

Contents from several disciplines, such as economics, finance, and statistics, are included in the module.

Subject specific skills

Estimate quantitative models and conduct statistical inference
Analyze different data structures using relevant models

Transferable skills

Problem solving